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"timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/ConnectivityShowcase", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "26": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "master", - "id": 26, - "name": "cloud-harness test edit", - "repository_type": "github", - "summary": "", - "tags": [], - "timestamp_created": "2023-01-18 12:00:52.711622+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/MetaCell/cloud-harness", - "user": { - "email": "filippo+8@metacell.us", - "first_name": "Filippo", - "id": "a2514035-c47f-4d8a-b22b-081d91a5ce6b", - "last_name": "Ledda", - "username": "a" - }, - "user_id": "a2514035-c47f-4d8a-b22b-081d91a5ce6b" - }, - "27": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 27, - "name": "NetPyNE Showcase", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 620, - "tag": "Python" - } - ], - "timestamp_created": "2023-01-18 15:17:18.567996+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/NetPyNEShowcase", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "29": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 29, - "name": "OpenCortex", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 610, - "tag": "Generic" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 604, - "tag": "Network" - }, - { - "id": 612, - "tag": "NeuroML2" - }, - { - "id": 540, - "tag": "OSBv1" - } - ], - "timestamp_created": "2023-02-14 11:03:13.310264+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/OpenCortex", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "34": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.250624.0409", - "id": 34, - "name": "Physiological Properties and Behavioral Correlates of Hippocampal Granule Cells and Mossy Cells", - "repository_type": "dandi", - "summary": "Data from \"Physiological Properties and Behavioral Correlates of Hippocampal Granule Cells and Mossy Cells\" Senzai, Buzsaki, Neuron 2017. Electrophysiology recordings of hippocampus during theta maze exploration.", - "tags": [ - { - "id": 11, - "tag": "cell types" - }, - { - "id": 12, - "tag": "current source density" - }, - { - "id": 13, - "tag": "laminar recordings" - }, - { - "id": 14, - "tag": "oscillations" - }, - { - "id": 15, - "tag": "mossy cells" - }, - { - "id": 16, - "tag": "granule cells" - }, - { - "id": 17, - "tag": "optogenetics" - }, - { - "id": 180, - "tag": "DANDI:000003" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 08:41:14.190981+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000003/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "36": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.220126.1852", - "id": 36, - "name": "A NWB-based dataset and processing pipeline of human single-neuron activity during a declarative memory task", - "repository_type": "dandi", - "summary": "A challenge for data sharing in systems neuroscience is the multitude of different data formats used. Neurodata Without Borders: Neurophysiology 2.0 (NWB:N) has emerged as a standardized data format for the storage of cellular-level data together with meta-data, stimulus information, and behavior. A key next step to facilitate NWB:N adoption is to provide easy to use processing pipelines to import/export data from/to NWB:N. Here, we present a NWB-formatted dataset of 1863 single neurons recorded from the medial temporal lobes of 59 human subjects undergoing intracranial monitoring while they performed a recognition memory task. We provide code to analyze and export/import stimuli, behavior, and electrophysiological recordings to/from NWB in both MATLAB and Python. The data files are NWB:N compliant, which affords interoperability between programming languages and operating systems. This combined data and code release is a case study for how to utilize NWB:N for human single-neuron recordings and enables easy re-use of this hard-to-obtain data for both teaching and research on the mechanisms of human memory.", - "tags": [ - { - "id": 18, - "tag": "cognitive neuroscience" - }, - { - "id": 19, - "tag": "data standardization" - }, - { - "id": 20, - "tag": "decision making" - }, - { - "id": 21, - "tag": "declarative memory" - }, - { - "id": 22, - "tag": "neurophysiology" - }, - { - "id": 23, - "tag": "neurosurgery" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 25, - "tag": "open source" - }, - { - "id": 26, - "tag": "single-neurons" - }, - { - "id": 169, - "tag": "DANDI:000004" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 77, - "tag": "Human" - } - ], - "timestamp_created": "2023-02-16 09:18:33.574492+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000004/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "37": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.220126.1853", - "id": 37, - "name": "Electrophysiology data from thalamic and cortical neurons during somatosensation", - "repository_type": "dandi", - "summary": "intracellular and extracellular electrophysiology recordings performed on mouse barrel cortex and ventral posterolateral nucleus (vpm) in whisker-based object locating task.", - "tags": [ - { - "id": 171, - "tag": "DANDI:000005" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 09:19:07.203113+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000005/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "38": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.220126.1855", - "id": 38, - "name": "Mouse anterior lateral motor cortex (ALM) in delay response task", - "repository_type": "dandi", - "summary": "Extracellular electrophysiology recordings performed on mouse anterior lateral motor cortex (ALM) in delay response task. Neural activity from two neuron populations, pyramidal track upper and lower, were characterized, in relation to movement execution. Some files, as originally (re)distributed from e.g. http://datasets.datalad.org/?dir=/labs/svoboda/Economo_2018 were found to be broken and would not be able available among reorganized files under sub-* directories.", - "tags": [ - { - "id": 173, - "tag": "DANDI:000006" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 09:19:10.841707+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000006/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "39": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.220126.1903", - "id": 39, - "name": "A cortico-cerebellar loop for motor planning", - "repository_type": "dandi", - "summary": "Extracellular recording in ALM", - "tags": [ - { - "id": 174, - "tag": "DANDI:000007" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 09:19:14.142177+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000007/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "40": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.211014.0809", - "id": 40, - "name": "Phenotypic variation within and across transcriptomic cell types in mouse motor cortex", - "repository_type": "dandi", - "summary": "We used Patch-seq to combine patch-clamp recording, biocytin staining, and single-cell RNA sequencing of over 1300 neurons in adult mouse motor cortex, providing a comprehensive morpho-electric annotation of almost all transcriptomically defined neural cell types. Contained in this dandiset are the intracellular electrophysiological recordings. See Dandiset #35 for an additional dataset, recorded under the physiological temperature. ", - "tags": [ - { - "id": 27, - "tag": "Patch-seq" - }, - { - "id": 8, - "tag": "cortex" - }, - { - "id": 28, - "tag": "motor cortex" - }, - { - "id": 29, - "tag": "mouse" - }, - { - "id": 188, - "tag": "DANDI:000008" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-02-16 09:19:19.251485+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000008/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "41": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.220126.1903", - "id": 41, - "name": "Maintenance of persistent activity in a frontal thalamocortical loop", - "repository_type": "dandi", - "summary": "We recorded spikes from the ALM and thalamus during tactile discrimination with a delayed directional response. Here we show that, similar to ALM neurons, thalamic neurons exhibited selective persistent delay activity that predicted movement direction. Unilateral photoinhibition of delay activity in the ALM or thalamus produced contralesional neglect. Photoinhibition of the thalamus caused a short-latency and near-complete collapse of ALM activity. Similarly, photoinhibition of the ALM diminished thalamic activity. Our results show that the thalamus is a circuit hub in motor preparation and suggest that persistent activity requires reciprocal excitation across multiple brain areas.", - "tags": [ - { - "id": 189, - "tag": "DANDI:000009" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 09:19:21.525200+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000009/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "42": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.220126.1905", - "id": 42, - "name": "A motor cortex circuit for motor planning and movement", - "repository_type": "dandi", - "summary": "Data from \"A motor cortex circuit for motor planning and movement\" Li et al. Nature 2015", - "tags": [ - { - "id": 190, - "tag": "DANDI:000010" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 09:19:23.705784+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000010/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "43": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.220126.1907", - "id": 43, - "name": "Robust neuronal dynamics in premotor cortex during motor planning", - "repository_type": "dandi", - "summary": "Data from \"Robust neuronal dynamics in premotor cortex during motor planning\" Nature 2016", - "tags": [ - { - "id": 191, - "tag": "DANDI:000011" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 09:19:25.970388+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000011/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "44": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 44, - "name": "Kriegstein2020", - "repository_type": "dandi", - "summary": "Data from the Kriegstein Lab as part of the BICCN", - "tags": [ - { - "id": 192, - "tag": "DANDI:000012" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 77, - "tag": "Human" - } - ], - "timestamp_created": "2023-02-16 09:19:29.045161+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000012/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "45": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.220126.2143", - "id": 45, - "name": "Low-noise encoding of active touch by layer 4 in the somatosensory cortex", - "repository_type": "dandi", - "summary": "Data from \"Low-noise encoding of active touch by layer 4 in the somatosensory cortex\" Hires, Gutnisky et al. Elife 2015", - "tags": [ - { - "id": 195, - "tag": "DANDI:000013" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 09:19:36.872738+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000013/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "46": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.220126.1914", - "id": 46, - "name": "A Map of Anticipatory Activity in Mouse Motor Cortex", - "repository_type": "dandi", - "summary": "Data from \"A Map of Anticipatory Activity in Mouse Motor Cortex\" Chen et al. Neuron 2017", - "tags": [ - { - "id": 196, - "tag": "DANDI:000015" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 09:19:39.628271+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000015/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "47": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 47, - "name": "Excitatory and inhibitory subnetworks are equally selective during decision-making and emerge simultaneously during learning", - "repository_type": "dandi", - "summary": "This package contains data, in NWB (Neurodata Without Borders) format, from the 4 mice included in \"Najafi, Farzaneh, Gamaleldin F. Elsayed, Robin Cao, Eftychios Pnevmatikakis, Peter E. Latham, John P. Cunningham, and Anne K. Churchland. \"Excitatory and inhibitory subnetworks are equally selective during decision-making and emerge simultaneously during learning.\" Neuron 105, no. 1 (2020): 165-179.\u201d Each NWB file represents the data and metadata associated with one recording session. In each NWB file, the metadata related to the session (mouse name, session date/time, lab/institution name, etc.) can be found under \"general\". Information related to ROI-segmentation such as ROI mask, ROI type (excitatory or inhibitory), poor or good quality, etc. can be found under \"modules/Image-Segmentation/pln-seg\". Trial information (e.g. start, end times, trial types, trial outcomes, etc.) can be found under \"trials\". Recorded trial-segmented neuronal responses aligned to different time event (e.g. stimulus start, animal choice, etc.) can be found under \"modules/ Trial-based-Segmentation\". A jupyter notebook presenting in detail how to work with NWB files is provided at https://github.com/ttngu207/najafi-2018-nwb/blob/master/notebooks/Najafi-2018_example.ipynb.", - "tags": [ - { - "id": 197, - "tag": "DANDI:000016" - }, - { - "id": 181, - "tag": "DANDI" - } - ], - "timestamp_created": "2023-02-16 09:19:48.465132+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000016/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "48": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.240329.1926", - "id": 48, - "name": "Distributed coding of choice, action and engagement across the mouse brain", - "repository_type": "dandi", - "summary": "Data from \"Distributed coding of choice, action and engagement across the mouse brain\" Steinmetz et. al Nature 2019", - "tags": [ - { - "id": 30, - "tag": "neuropixels" - }, - { - "id": 198, - "tag": "DANDI:000017" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 09:19:49.743090+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000017/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "49": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.220126.2148", - "id": 49, - "name": "Human ECoG speaking consonant-vowel syllables", - "repository_type": "dandi", - "summary": "The enclosed data is collected using a high-density 256-channel electrocorticography (ECoG) array implanted in human patients during treatment for epilepsy. The subjects are reading aloud consonant-vowel syllables from a list. The data was collected by Dr. Edward Chang and Dr. Kristofer Bouchard at the University of California, San Francisco, and curated by Dr. Kristofer Bouchard and Dr. Benjamin Dichter.\n\n\nData is organized by subject ID, and each file is a continuous recording session in Neurodata Without Borders (NWB) 2.0 format. Voltage traces are included for each of the recorded 256 channels. Microphone signal was recorded at the time but is removed for HIPAA compliance. Detailed hand-marked annotations are provided which mark what syllable was said, and the times of the start, consonant-vowel transition, and end of each syllable. A rest-period time is also included when the subject was silent, which can be used as a baseline. Hand-marked anatomical labels are included for electrodes in the relevant brain regions. All dates have been removed for HIPAA compliance and replaced with Jan 1, 1900.", - "tags": [ - { - "id": 31, - "tag": "electrocorticography (ECoG)" - }, - { - "id": 32, - "tag": "speech production" - }, - { - "id": 199, - "tag": "DANDI:000019" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 77, - "tag": "Human" - } - ], - "timestamp_created": "2023-02-16 09:19:50.974579+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000019/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "50": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.210913.1639", - "id": 50, - "name": "Patch-seq recordings from mouse visual cortex", - "repository_type": "dandi", - "summary": "Whole-cell Patch-seq recordings from neurons of the mouse visual cortex from the Allen Institute for Brain Science, released in June 2020. The majority of cells in this dataset are GABAergic interneurons, but there are also a small number of glutamatergic neurons from layer 2/3 of the mouse visual cortex.", - "tags": [ - { - "id": 27, - "tag": "Patch-seq" - }, - { - "id": 29, - "tag": "mouse" - }, - { - "id": 33, - "tag": "visual cortex" - }, - { - "id": 34, - "tag": "interneuron" - }, - { - "id": 200, - "tag": "DANDI:000020" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 09:19:52.885831+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000020/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "51": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 51, - "name": "20191003_AIBS_mouse_ecephys_brain_observatory_1_1", - "repository_type": "dandi", - "summary": "Allen Institute October 2019 Mouse extracellular electrophysiology data approximately matching two-photon brain observatory stimulus set. \n\nFor more information, see https://portal.brain-map.org/explore/circuits/visual-coding-neuropixels\n\nData are subject to Allen Institute terms of use, available at: http://www.alleninstitute.org/legal/terms-use/", - "tags": [ - { - "id": 35, - "tag": "electrophysiology" - }, - { - "id": 36, - "tag": "life sciences" - }, - { - "id": 37, - "tag": "machine learning" - }, - { - "id": 38, - "tag": "neurobiology" - }, - { - "id": 39, - "tag": "signal processing" - }, - { - "id": 201, - "tag": "DANDI:000021" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 09:19:54.098887+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000021/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "52": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 52, - "name": "20191003_AIBS_mouse_ecephys_functional_connectivity", - "repository_type": "dandi", - "summary": "Allen Institute October 2019 Mouse extracellular electrophysiology data, collected under analogous conditions to the two-photon Brain Observatory imaging experiments, with mice shown a subset of stimuli with a higher number of repeats. \n\nFor more information, see https://portal.brain-map.org/explore/circuits/visual-coding-neuropixels\n\nData are subject to Allen Institute Terms of Use policy, available at: http://www.alleninstitute.org/legal/terms-use/\n", - "tags": [ - { - "id": 35, - "tag": "electrophysiology" - }, - { - "id": 36, - "tag": "life sciences" - }, - { - "id": 37, - "tag": "machine learning" - }, - { - "id": 38, - "tag": "neurobiology" - }, - { - "id": 39, - "tag": "signal processing" - }, - { - "id": 202, - "tag": "DANDI:000022" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 09:19:55.349426+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000022/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "53": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.210914.1900", - "id": 53, - "name": "Patch-seq recordings from human cortex (June 2020)", - "repository_type": "dandi", - "summary": "Whole-cell Patch-seq recordings from neurons of the human neocortex from the Allen Institute for Brain Science, released in June 2020.", - "tags": [ - { - "id": 27, - "tag": "Patch-seq" - }, - { - "id": 7, - "tag": "human" - }, - { - "id": 40, - "tag": "neocortex" - }, - { - "id": 203, - "tag": "DANDI:000023" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 77, - "tag": "Human" - }, - { - "id": 286, - "tag": "layer 2/3" - } - ], - "timestamp_created": "2023-02-16 09:19:56.683686+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000023/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "54": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 54, - "name": "Example intracellular ephys data from LNMC & BBP", - "repository_type": "dandi", - "summary": "This is a single session showing example intracellular electrophysiology data collected at the Laboratory of Neural Microcircuitry, EPFL. This session holds a variety of different stimulation patterns. \n\nIt also serves as an example of the hierarchy of metadata tables (/general/intracellular_ephys/) and groups (/data_organization) storing sweeps of a complex experimental protocol.\n\nLoad this file in pynwb with load_namespaces = True.\n", - "tags": [ - { - "id": 204, - "tag": "DANDI:000025" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 205, - "tag": "Rattus norvegicus - Norway rat" - } - ], - "timestamp_created": "2023-02-16 09:19:57.941421+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000025/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "55": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 55, - "name": "Human brain cell census for BA 44/45", - "repository_type": "dandi", - "summary": "Magnetic resonance imaging (MRI) is used to establish a macroscopic reference coordinate system of laminar and cytoarchitectural boundaries. Cell counting is obtained with both traditional immunohistochemistry, to provide a stereological gold standard, and with a custom-made inverted confocal light-sheet fluorescence microscope (LSM) for 3D imaging at cellular resolution. Finally, polarization-sensitive optical coherence tomography (PSOCT) enables registration of the distorted histological cell typing obtained with LSM to the MRI-based atlas coordinate system.", - "tags": [ - { - "id": 42, - "tag": "multi-modal imaging" - }, - { - "id": 43, - "tag": "MRI" - }, - { - "id": 44, - "tag": "OCT" - }, - { - "id": 45, - "tag": "SPIM" - }, - { - "id": 46, - "tag": "human cortex" - }, - { - "id": 47, - "tag": "Broca's area" - }, - { - "id": 48, - "tag": "Motor cortex" - }, - { - "id": 49, - "tag": "Stereology" - }, - { - "id": 206, - "tag": "DANDI:000026" - }, - { - "id": 181, - "tag": "DANDI" - } - ], - "timestamp_created": "2023-02-16 09:20:01.372666+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000026/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "56": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.210831.2033", - "id": 56, - "name": "Test dataset for testing dandi-cli.", - "repository_type": "dandi", - "summary": "Should be ignored by regular mortals.\n\nATM contains only a few files from http://github.com/dandi-datasets/nwb_test_data which more or less appropriate (do not lack critical metadata) for testing", - "tags": [ - { - "id": 50, - "tag": "development" - }, - { - "id": 207, - "tag": "DANDI:000027" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 205, - "tag": "Rattus norvegicus - Norway rat" - } - ], - "timestamp_created": "2023-02-16 09:20:02.723903+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000027/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "57": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 57, - "name": "Simulated cortical Neuropixels recording with ground truth", - "repository_type": "dandi", - "summary": "A 10 minute Neuropixels recording simulated using the MEArec Python package (Buccino et al., 2020). The recording contains the spiking activity of 250 biophysically detailed neurons (200 excitatory and 50 inhibitory cells from the Neocortical Micro Circuit Portal with independent Poisson firing patterns, and additive Gaussian noise with 10uV standard deviation.", - "tags": [ - { - "id": 208, - "tag": "DANDI:000028" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 09:20:04.006007+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000028/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "58": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.231017.2004", - "id": 58, - "name": "Test dataset for development purposes", - "repository_type": "dandi", - "summary": "Should be ignored by regular mojgjhhj. fjrtalddgdfgdfgs, and not relied upon being static or ever correct.\n\u0414\u043e\u043b\u0436\u043d\u043e \u0431\u044b\u0442\u044c \u0432\u0432\u0435\u0434\u0435\u043d\u043e \u043d\u0430 \u0410\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u043e\u043c, \u043d\u043e \u043f\u043e\u0447\u0435\u043c\u0443 \u0431\u044b \u043d\u0430\u043c \u043d\u0435 \u043f\u0440\u043e\u0432\u0435\u0440\u0438\u0442\u044c \u0432\u0441\u044e \u044d\u0442\u0443 \u043a\u0443\u0445\u043d\u044e \n\u0394\u0419\u05e7\u0645\u0e57\u3042\nabcdefghi", - "tags": [ - { - "id": 50, - "tag": "development" - }, - { - "id": 209, - "tag": "DANDI:000029" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 210, - "tag": "Macaca mulatta - Rhesus monkey" - } - ], - "timestamp_created": "2023-02-16 09:20:05.356536+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000029/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "59": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.211030.0713", - "id": 59, - "name": "SpikeInterface, a unified framework for spike sorting", - "repository_type": "dandi", - "summary": "SpikeInterface is a Python framework designed to make the analysis of extracellular electrophysiology data more accessible and standardized. The SpikeInterface documentation can be found at https://spikeinterface.readthedocs.io/en/latest/.\n\nThis dataset contains all recordings analysed in the paper: \"SpikeInterface, a unified framework for spike sorting.\" Alessio P. Buccino, Cole L. Hurwitz, Samuel Garcia, Jeremy Magland, Joshua H. Siegle, Roger Hurwitz, Matthias H. Hennig, eLife - doi: https://doi.org/10.7554/eLife.61834; ", - "tags": [ - { - "id": 51, - "tag": "Spike Sorting" - }, - { - "id": 52, - "tag": "extracellular electrophysiology" - }, - { - "id": 211, - "tag": "DANDI:000034" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 09:20:06.624265+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000034/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "60": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.211014.0808", - "id": 60, - "name": "Temperature-controlled intracellular Patch-seq recordings in mouse motor cortex", - "repository_type": "dandi", - "summary": " We used Patch-seq to combine patch-clamp recording, biocytin staining, and single-cell RNA sequencing of over 1300 neurons in adult mouse motor cortex, providing a comprehensive morpho-electric annotation of almost all transcriptomically defined neural cell types. This dataset contains rectangular stimulation of cells under temperature-controlled conditions (34 \u2103). See Dandiset #8 for the main dataset, recorded under the room temperature.", - "tags": [ - { - "id": 27, - "tag": "Patch-seq" - }, - { - "id": 29, - "tag": "mouse" - }, - { - "id": 8, - "tag": "cortex" - }, - { - "id": 28, - "tag": "motor cortex" - }, - { - "id": 212, - "tag": "DANDI:000035" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 09:20:07.951830+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000035/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "61": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230515.1917", - "id": 61, - "name": "Allen Institute Openscope - Measuring Stimulus-Evoked Neurophysiological Differentiation in Distinct Populations of Neurons in Mouse Visual Cortex", - "repository_type": "dandi", - "summary": "Despite significant progress in understanding neural coding, it remains unclear how the coordinated activity of large populations of neurons relates to what an observer actually perceives. Since neurophysiological differences must underlie differences among percepts, differentiation analysis-quantifying distinct patterns of neurophysiological activity-has been proposed as an \"inside-out\" approach that addresses this question. This methodology contrasts with \"outside-in\" approaches such as feature tuning and decoding analyses, which are defined in terms of extrinsic experimental variables. Here, we used two-photon calcium imaging in mice of both sexes to systematically survey stimulus-evoked neurophysiological differentiation (ND) in excitatory neuronal populations in layers (L)2/3, L4, and L5 across five visual cortical areas (primary, lateromedial, anterolateral, posteromedial, and anteromedial) in response to naturalistic and phase-scrambled movie stimuli. We find that unscrambled stimuli evoke greater ND than scrambled stimuli specifically in L2/3 of the anterolateral and anteromedial areas, and that this effect is modulated by arousal state and locomotion. By contrast, decoding performance was far above chance and did not vary substantially across areas and layers. Differentiation also differed within the unscrambled stimulus set, suggesting that differentiation analysis may be used to probe the ethological relevance of individual stimuli.\n\nFor more details, consult the associated publication : https://doi.org/10.1523/eneuro.0280-21.2021", - "tags": [ - { - "id": 53, - "tag": "two photon imaging" - }, - { - "id": 54, - "tag": "visual stimuli" - }, - { - "id": 55, - "tag": "mice" - }, - { - "id": 56, - "tag": "openscope" - }, - { - "id": 213, - "tag": "DANDI:000036" - }, - { - "id": 181, - "tag": "DANDI" - } - ], - "timestamp_created": "2023-02-16 09:20:09.212509+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000036/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "62": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.240209.1623", - "id": 62, - "name": "Allen Institute Openscope - Responses to inconsistent stimuli in somata and distal apical dendrites in primary visual cortex", - "repository_type": "dandi", - "summary": "This dataset was collected for the Credit Assignment project, as part of the Allen Institute for Brain Science's OpenScope project. The most up-to-date links to the publications associated with this dataset (a dataset descriptor paper and an analysis paper) can be found by going to the metadata Github repository, linked below.\n\nBriefly, mice were habituated to head-fixation on a running disc over a period of 11 days. Over the last 6 days, mice were presented with repeating stimulus sequences, with consistent features. These sessions are not included in the dataset, as imaging was not performed. Following habituation, sequences with inconsistent features were introduced over three sessions, while two-photon calcium imaging was performed in pyramidal neurons of primary visual cortex (VisP). Specifically, recordings for each mouse were made in one of four planes: L2/3 somata, L5 somata, L2/3 distal apical dendrites or L5 distal apical dendrites. As mice gained experience with the inconsistent sequences, responses to these events were found to evolve in opposite ways in the dendritic and somatic compartments.\n\nThe dataset includes 50 sessions total, recorded in 13 subjects, with at least 3 sessions per subject. Each session file includes: **(1)** ROI dF/F traces, **(2)** ROI masks, **(3)** ROI tracking information (where applicable), **(4)** running velocity traces, **(5)** pupil diameter traces, **(6)** pupil centroid position traces in x and y, **(7)** stimulus parameters. Note that gaze was not computed for this dataset. What is provided is the position of the pupil centroid in the pupil recording videos. In addition, a second, slightly larger version of each file was created, also including: **(8)** stimulus frame images (identifiable by `+image` in the file name). Lastly, a third, much larger version of each file was created, also including: **(9)** the motion corrected imaging stack (identifiable by `_obj-raw` in the file name).\n\nCertain sessions in this dataset were excluded from our analyses for quality control reasons. They are provided here, as the reasons we excluded them from our analyses may not pose a problem for all use cases. The metadata Github repository, linked below, provides detailed information about each session. \n\nRelease notes for each version of the dataset are provided in the CHANGES file.", - "tags": [ - { - "id": 57, - "tag": "learning" - }, - { - "id": 40, - "tag": "neocortex" - }, - { - "id": 58, - "tag": "pyramidal neurons" - }, - { - "id": 59, - "tag": "distal apical dendrites" - }, - { - "id": 60, - "tag": "somata" - }, - { - "id": 61, - "tag": "L2/3" - }, - { - "id": 62, - "tag": "L5" - }, - { - "id": 63, - "tag": "two-photon calcium imaging" - }, - { - "id": 64, - "tag": "mouse VisP" - }, - { - "id": 65, - "tag": "prediction" - }, - { - "id": 66, - "tag": "credit assignment" - }, - { - "id": 214, - "tag": "DANDI:000037" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-02-16 09:20:10.537814+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000037/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "63": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230223.1216", - "id": 63, - "name": "Allen Institute \u2013 Contrast tuning in mouse visual cortex with calcium imaging", - "repository_type": "dandi", - "summary": "A two photon calcium imaging dataset from Allen Institute measuring responses to full-field drifting gratings (approx. 120x90 degrees of visual space) of 8 directions and 6 contrasts (5%, 10%, 20%, 40%, 60%, 80%). Mouse Cre lines expressing GCaMP6f were imaged to record responses of pyramidal neurons across cortical layers (Cux2: layer 2/3; Rorb: layer 4; Rbp4: layer 5; Ntsr1: layer 6) as well as inhibitory interneurons (Vip and Sst). All experimental sessions took place on the same data collection pipeline as the Allen Brain Observatory (see http://observatory.brain-map.org/visualcoding) and have the same visual stimulus monitor calibration and positioning, two photon imaging systems and image processing pipeline, and running wheel to track locomotion.\n\n Data are subject to Allen Institute Terms of Use policy, available at: http://www.alleninstitute.org/legal/terms-use/", - "tags": [ - { - "id": 67, - "tag": "vision" - }, - { - "id": 33, - "tag": "visual cortex" - }, - { - "id": 68, - "tag": "inhibition" - }, - { - "id": 69, - "tag": "inhibitory circuits" - }, - { - "id": 70, - "tag": "circuit dynamics" - }, - { - "id": 71, - "tag": "gain control" - }, - { - "id": 215, - "tag": "DANDI:000039" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 09:20:11.964425+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000039/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "64": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.250624.0419", - "id": 64, - "name": "Network Homeostasis and State Dynamics of Neocortical Sleep", - "repository_type": "dandi", - "summary": "Data was recorded using silicon probe electrodes in the frontal cortices of male Long Evans rats between 4-7 months of age. The design was to have no specific behavior, task or stimulus, rather the animal was left alone in it\u2019s home cage (which it lives in at all\ntimes). Data includes both local field potentials (LFP) and spikes. 11 total animals, 27 recording sessions, 1360 total units recorded, 1121 units considered stable, 995 putative excitatory units and 126 putative inhibitory units. Only recordings including a \u201cWAKE-SLEEP\u201d episode wherein at least 7 minutes of wake are followed by 20 minutes of sleep. On average 2 such WAKE-SLEEP episodes per recording session. ", - "tags": [ - { - "id": 72, - "tag": "Firing patterns" - }, - { - "id": 73, - "tag": "Sleep/awake states" - }, - { - "id": 74, - "tag": "Sleep stages" - }, - { - "id": 75, - "tag": "Homeostasis" - }, - { - "id": 216, - "tag": "DANDI:000041" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 217, - "tag": "Brown rat" - } - ], - "timestamp_created": "2023-02-16 09:20:13.212226+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000041/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "65": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 65, - "name": "Human, macaque, and mouse L5 pyramidal neuron physiology", - "repository_type": "dandi", - "summary": "Patch clamp recordings from human premotor cortex, macaque M1 and mouse MOp.", - "tags": [ - { - "id": 27, - "tag": "Patch-seq" - }, - { - "id": 48, - "tag": "Motor cortex" - }, - { - "id": 76, - "tag": "Betz cell" - }, - { - "id": 77, - "tag": "Human" - }, - { - "id": 78, - "tag": "Macaque" - }, - { - "id": 79, - "tag": "Mouse" - }, - { - "id": 218, - "tag": "DANDI:000043" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 09:20:14.587463+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000043/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "66": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.250624.0426", - "id": 66, - "name": "Diversity in neural firing dynamics supports both rigid and learned hippocampal sequences", - "repository_type": "dandi", - "summary": "This data set is composed of eight bilateral silicon-probe multi-cellular electrophysiological recordings performed on four male Long-Evans rats in the Buzs\u00e1ki lab at NYU. These recordings were performed to assess the effect of novel spatial learning on hippocampal CA1 neural firing and LFP patterns in na\u00efve animals. Each session consisted of a long (~4 hour) PRE rest/sleep epoch home-cage recordings performed in a familiar room, followed by a Novel MAZE running epoch (~45 minutes) in which the animals were transferred to a novel room, and water-rewarded to run on a novel maze. These mazes were either A) a wooden 1.6m linear platform, B) a wooden 1m diameter circular platform or C) a 2m metal linear platform. Animals were rewarded either at both ends of the linear platform, or at a predetermined location on the circular platform. The animal was gently encouraged to run unidirectionally on the circular platform. After the MAZE epochs the animals were transferred back to their home-cage in the familiar room where a long (~4 hour) POST rest/sleep was recorded. All eight sessions were concatenated from the PRE, MAZE, and POST recording epochs. In addition to hippocampal electrophysiological recordings, neck EMG and head-mounted accelerometer signals were recorded, and the animal\u2019s position during MAZE running epochs was tracked via head-mounted LEDs.", - "tags": [ - { - "id": 219, - "tag": "DANDI:000044" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 217, - "tag": "Brown rat" - } - ], - "timestamp_created": "2023-02-16 09:20:15.832550+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000044/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "67": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.211209.1413", - "id": 67, - "name": "IBL behavioral data", - "repository_type": "dandi", - "summary": "This dataset is a result of a multi-institution, cross country collaboration of labs, called International Brain Laboratory conducting standardized experiments on decision-making in mice. In the experiment, mice are shown a visual grating on screen with varying levels of contrast, and they are trained to rotate a wheel to move the on-screen stimulus from either side of their visual field to the center. These NWB files contain datasets corresponding to this wheel movement, camera footage of mice and information about the visual stimulus at every trial. \nPaper: \nAguillon, V., Angelaki, D., Bayer, H. M., Bonacchi, N., Carandini, M., Cazettes, F., Churchland, A. K., Chapuis, G., Dan, Y., Dewitt, E., Faulkner, M., Hamish, F., Haetzel, L., Hausser, M., Hofer, S., Hu, F., Khanal, A., Krasniak, C., Laranjeira, I., \u2026 Zador, A. (2020). A standardized and reproducible method to measure decision-making in mice. BioRxiv, 2020.01.17.909838. https://doi.org/10.1101/2020.01.17.909838", - "tags": [ - { - "id": 80, - "tag": "International Brain Laboratory" - }, - { - "id": 220, - "tag": "DANDI:000045" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 09:20:17.901715+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000045/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "68": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 68, - "name": "Electrical and optical physiology in in vivo population-scale two-photon calcium imaging", - "repository_type": "dandi", - "summary": "Spiking activity and simultaneously recorded fluorescence signals in transgenic mice expressing GCaMP6\n\nWe present a dataset consisting of simultaneously measured fluorescence and spiking activity of pyramidal neurons in layer 2/3 of primary visual cortex in transgenic mouse lines expressing genetically-encoded calcium indicators (GECIs) GCaMP6s or GCaMP6f.\n\nReference: https://portal.brain-map.org/explore/circuits/oephys", - "tags": [ - { - "id": 221, - "tag": "DANDI:000048" - }, - { - "id": 181, - "tag": "DANDI" - } - ], - "timestamp_created": "2023-02-16 09:20:19.191031+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000048/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "69": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230223.1424", - "id": 69, - "name": "Allen Institute \u2013 TF x SF tuning in mouse visual cortex with calcium imaging", - "repository_type": "dandi", - "summary": "A two photon calcium imaging dataset from Allen Institute measuring responses to full-field drifting gratings (approx. 120x90 degrees of visual space) moving in 4 different directions, at 5 spatial frequencies (0.02, 0.04, 0.08, 0.16, 0.32 cpd), and 5 temporal frequencies (0.5, 1, 2, 4, 8 Hz). The ratio of TF/SF is speed (deg/sec) and the extent to which visual neurons exhibit speed tuning has been shown to vary across some cortical areas (Andermann et al. 2011).\nMouse Cre lines expressing GCaMPf were imaged to record responses of pyramidal neurons across cortical layers (Cux2: layer 2/3; Rorb: layer 4; Rbp4: layer 5; Ntsr1: layer 6) as well as somatostatin inhibitory interneurons (Sst). All Cre lines were imaged in VISp, and some (Cux2 and Sst) were also imaged in VISl, VISal, VISpm, VISam, and VISrl. All experimental sessions took place on the same data collection pipeline as the Allen Brain Observatory (see de Vries, Lecoq, Buice et al. 2020) and have the same visual stimulus monitor calibration and positioning, two photon imaging systems and image processing pipeline, and running wheel to track locomotion. Data are subject to Allen Institute Terms of Use policy, available at: http://www.alleninstitute.org/legal/terms-use/", - "tags": [ - { - "id": 79, - "tag": "Mouse" - }, - { - "id": 222, - "tag": "2-photon calcium imaging" - }, - { - "id": 33, - "tag": "visual cortex" - }, - { - "id": 223, - "tag": "DANDI:000049" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 09:20:20.713691+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000049/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "70": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 70, - "name": "Allen Institute - Run Tuning in the Mouse Visual Cortex", - "repository_type": "dandi", - "summary": "Allen Institute for Brain Science, MindScope Project.", - "tags": [ - { - "id": 224, - "tag": "DANDI:000050" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 09:20:21.982025+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000050/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "71": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 71, - "name": "pons8-yo_16xdownsampled", - "repository_type": "dandi", - "summary": "Downsampled image of pons slice #8 stained with a nuclear dye. For testing only.\n", - "tags": [ - { - "id": 225, - "tag": "DANDI:000051" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 77, - "tag": "Human" - } - ], - "timestamp_created": "2023-02-16 09:20:23.262047+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000051/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "72": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 72, - "name": "Pons8-BIDS-16xdownsampled", - "repository_type": "dandi", - "summary": "A test upload of Pons8 YO and Lectin, downsampled 16x using the BIDS schema", - "tags": [ - { - "id": 226, - "tag": "DANDI:000052" - }, - { - "id": 181, - "tag": "DANDI" - } - ], - "timestamp_created": "2023-02-16 09:20:24.523327+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000052/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "73": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.210819.0345", - "id": 73, - "name": "Recordings from medial entorhinal cortex during linear track and open exploration", - "repository_type": "dandi", - "summary": "This dataset contains two task types. The first is tetrode recordings from medial entorhinal cortex during open field navigation with simultaneous inertial measurements of the head, and the second is Neuropixel recordings from medial entorhinal cortex during navigation down a virtual linear track with simultaneous eye measurements.", - "tags": [ - { - "id": 81, - "tag": "neuropixel" - }, - { - "id": 82, - "tag": "entorhinal cortex" - }, - { - "id": 228, - "tag": "DANDI:000053" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 09:20:25.810154+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000053/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "74": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.210819.1547", - "id": 74, - "name": "Plitt & Giocomo (2021) Experience Dependent Contextual Codes in the Hippocampus. Nat Neuro", - "repository_type": "dandi", - "summary": "Data included in Plitt & Giocomo (2021) Nature Neuroscience", - "tags": [ - { - "id": 229, - "tag": "DANDI:000054" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 09:20:27.079189+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000054/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "75": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.220127.0436", - "id": 75, - "name": "AJILE12: Long-term naturalistic human intracranial neural recordings and pose", - "repository_type": "dandi", - "summary": "Understanding the neural basis of human movement in naturalistic scenarios is critical for expanding neuroscience research beyond constrained laboratory paradigms. The neural correlates of unstructured, spontaneous movements in completely naturalistic settings have rarely been studied, due in large part to a lack of available data. Here, we present our Annotated Joints in Long-term Electrocorticography for 12 human participants (AJILE12) dataset, the largest human neurobehavioral dataset that is publicly available; the dataset was recorded opportunistically during passive clinical epilepsy monitoring. AJILE12 includes synchronized intracranial neural recordings and upper body pose trajectories across 55 semi-continuous days of naturalistic movements, along with relevant metadata, including thousands of wrist movement events and annotated behavioral states. Neural recordings are available at 500 Hz from at least 64 electrodes per participant, for a total of 1280 hours. Pose trajectories at 9 upper-body keypoints, including wrist, elbow, and shoulder joints, were sampled at 30 frames per second and estimated from 118 million video frames. In adherence with the FAIR data principles, we have shared AJILE12 on The Dandi Archive in the Neurodata Without Borders (NWB) data standard and developed a browser-based dashboard to facilitate data exploration and reuse.", - "tags": [ - { - "id": 230, - "tag": "DANDI:000055" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 77, - "tag": "Human" - } - ], - "timestamp_created": "2023-02-16 09:20:28.417399+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000055/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "77": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 77, - "name": "MITU01 Dataset", - "repository_type": "dandi", - "summary": "7T MR structural images. B0 and B1+ code corrected parameter maps. ", - "tags": [ - { - "id": 231, - "tag": "DANDI:000058" - }, - { - "id": 181, - "tag": "DANDI" - } - ], - "timestamp_created": "2023-02-16 09:20:30.911062+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000058/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "78": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.250624.0444", - "id": 78, - "name": "Cooling of Medial Septum Reveals Theta Phase Lag Coordination of Hippocampal Cell Assemblies", - "repository_type": "dandi", - "summary": "Hippocampal theta oscillations coordinate neuronal firing to support memory and spatial navigation. The medial septum (MS) is critical in theta generation by two possible mechanisms: either a unitary \u201cpacemaker\u201d timing signal is imposed on the hippocampal system, or it may assist in organizing target subcircuits within the phase space of theta oscillations. We used temperature manipulation of the MS to test these models. Cooling of the MS reduced both theta frequency and power and was associated with an enhanced incidence of errors in a spatial navigation task, but it did not affect spatial correlates of neurons. MS cooling decreased theta frequency oscillations of place cells and reduced distance-time compression but preserved distance-phase compression of place field sequences within the theta cycle. Thus, the septum is critical for sustaining precise theta phase coordination of cell assemblies in the hippocampal system, a mechanism needed for spatial memory.", - "tags": [ - { - "id": 232, - "tag": "DANDI:000059" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 205, - "tag": "Rattus norvegicus - Norway rat" - } - ], - "timestamp_created": "2023-02-16 09:20:32.180782+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000059/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "79": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 79, - "name": "Dataset for Finkelstein, Fontolan et al. \"Attractor dynamics gate cortical information flow during decision-making\"", - "repository_type": "dandi", - "summary": "Extracellular electrophysiology recordings in anterior lateral motor cortex and in vibrissal sensory cortex in mice trained to detect optogenetic stimulation of the vibrissal sensory cortex.\n\nThe data analysis code for this dataset is available here: \n https://github.com/arsenyf/FinkelsteinFontolan_2021NN", - "tags": [ - { - "id": 28, - "tag": "motor cortex" - }, - { - "id": 52, - "tag": "extracellular electrophysiology" - }, - { - "id": 84, - "tag": "decision-making" - }, - { - "id": 85, - "tag": "attractor" - }, - { - "id": 86, - "tag": "optogenetic stimulation" - }, - { - "id": 233, - "tag": "DANDI:000060" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 09:20:33.441243+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000060/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "80": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.250624.0426", - "id": 80, - "name": "Reactivations of emotional memory in the hippocampus\u2013amygdala system during sleep", - "repository_type": "dandi", - "summary": "The consolidation of context-dependent emotional memory requires communication between the hippocampus and the basolateral amygdala (BLA), but the mechanisms of this process are unknown. We recorded neuronal ensembles in the hippocampus and BLA while rats learned the location of an aversive air puff on a linear track, as well as during sleep before and after training. We found coordinated reactivations between the hippocampus and the BLA during non-REM sleep following training. These reactivations peaked during hippocampal sharp wave\u2013ripples (SPW-Rs) and involved a subgroup of BLA cells positively modulated during hippocampal SPW-Rs. Notably, reactivation was stronger for the hippocampus\u2013BLA correlation patterns representing the run direction that involved the air puff than for the 'safe' direction. These findings suggest that consolidation of contextual emotional memory occurs during ripple-reactivation of hippocampus\u2013amygdala circuits.", - "tags": [ - { - "id": 234, - "tag": "DANDI:000061" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 217, - "tag": "Brown rat" - } - ], - "timestamp_created": "2023-02-16 09:20:34.702157+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000061/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "81": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.221025.1735", - "id": 81, - "name": "Simulation extension example", - "repository_type": "dandi", - "summary": "This is data produced by the Soltesz Lab NeuroH5 software (https://github.com/iraikov/neuroh5). The data has been converted to NWB using the ndx-simulation-output extension (https://github.com/catalystneuro/ndx-simulation-output).", - "tags": [ - { - "id": 235, - "tag": "DANDI:000064" - }, - { - "id": 181, - "tag": "DANDI" - } - ], - "timestamp_created": "2023-02-16 09:20:36.033326+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000064/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "82": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 82, - "name": "Polymer probe recordings from hippocampus (LFP), OFC, NAc, and mPFC", - "repository_type": "dandi", - "summary": "Recordings made by Jason Chung in the Frank laboratory at UCSF using 64 channel polymer probes across 4 regions in an animal resting in a box. Note that the position data is duplicated, one copy for each epoch.", - "tags": [ - { - "id": 88, - "tag": "polymer probe" - }, - { - "id": 35, - "tag": "electrophysiology" - }, - { - "id": 89, - "tag": "nucleus accumbens" - }, - { - "id": 90, - "tag": "medial prefrontal cortex" - }, - { - "id": 91, - "tag": "orbitofrontal cortex" - }, - { - "id": 92, - "tag": "hippocampus" - }, - { - "id": 93, - "tag": "sleep" - }, - { - "id": 287, - "tag": "rat" - }, - { - "id": 288, - "tag": "DANDI:000065" - }, - { - "id": 181, - "tag": "DANDI" - } - ], - "timestamp_created": "2023-02-16 09:20:37.248664+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000065/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "83": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 83, - "name": "Allen Mouse Common Coordinate Framework - Average Brain Template", - "repository_type": "dandi", - "summary": "The reference space or brain template was constructed as a population average of 1,675 young adult C57BL/6J mice brains imaged using serial two photon tomography (STPT) for the Allen Mouse Brain Connectivity Atlas. The average template was created from tissue autofluorescence detected in the red channel. To maximize input data and create a symmetrical atlas, each dataset was reflected across the midline, for a total of 3,350 (2 x 1,675) hemisphere datasets. Creation of the template followed a two-step iterative process: (1) We deformably registered each specimen to the current iteration of the template and computed an intensity average. (2) We then computed the average deformation field, inverted it, and applied it to the intensity average created in (1). This resulted in a volume with an average unbiased shape and intensity to be used as the template in the next iteration until convergence.\n\nThe axes the average template volume is a +X=Posterior, +Y=Inferior(Ventral) and +Z=Right frame with the origin at the corner of the volume.", - "tags": [ - { - "id": 237, - "tag": "DANDI:000066" - }, - { - "id": 181, - "tag": "DANDI" - } - ], - "timestamp_created": "2023-02-16 09:20:38.530897+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000066/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "84": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.250624.0419", - "id": 84, - "name": "Behavior-dependent short-term assembly dynamics in the medial prefrontal cortex", - "repository_type": "dandi", - "summary": "Although short-term plasticity is believed to play a fundamental role in cortical computation, empirical evidence bearing on its role during behavior is scarce. Here we looked for the signature of short-term plasticity in the fine-timescale spiking relationships of a simultaneously recorded population of physiologically identified pyramidal cells and interneurons, in the medial prefrontal cortex of the rat, in a working memory task. On broader timescales, sequentially organized and transiently active neurons reliably differentiated between different trajectories of the rat in the maze. On finer timescales, putative monosynaptic interactions reflected short-term plasticity in their dynamic and predictable modulation across various aspects of the task, beyond a statistical accounting for the effect of the neurons' co-varying firing rates. Seeking potential mechanisms for such effects, we found evidence for both firing pattern\u2013dependent facilitation and depression, as well as for a supralinear effect of presynaptic coincidence on the firing of postsynaptic targets.", - "tags": [ - { - "id": 238, - "tag": "DANDI:000067" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 217, - "tag": "Brown rat" - } - ], - "timestamp_created": "2023-02-16 09:20:39.777005+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000067/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "85": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 85, - "name": "Testing", - "repository_type": "dandi", - "summary": "Nothing to see", - "tags": [ - { - "id": 240, - "tag": "DANDI:000068" - }, - { - "id": 181, - "tag": "DANDI" - } - ], - "timestamp_created": "2023-02-16 09:20:40.956419+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000068/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "86": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 86, - "name": "Neural population dynamics during reaching", - "repository_type": "dandi", - "summary": "Monkeys recordings of Motor Cortex (M1) and dorsal Premotor Cortex (PMd) using two 96 channel high density Utah Arrays (Blackrock Microsystems) while performing reaching tasks with right hand.", - "tags": [ - { - "id": 241, - "tag": "DANDI:000070" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 242, - "tag": "Rhesus monkey" - } - ], - "timestamp_created": "2023-02-16 10:12:46.651580+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000070/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "87": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 87, - "name": "MGH19-1-021520", - "repository_type": "dandi", - "summary": "Pons section from acccession MGH 19-1_021520 stained with YOYO1", - "tags": [ - { - "id": 243, - "tag": "DANDI:000105" - }, - { - "id": 181, - "tag": "DANDI" - } - ], - "timestamp_created": "2023-02-16 10:12:48.004289+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000105/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "88": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 88, - "name": "IVSCC stimulus sets", - "repository_type": "dandi", - "summary": "Allen Institute for Brain Science IVSCC (In-vitro Single Cell Characterization) project stimulus sets stored in NWB format", - "tags": [ - { - "id": 35, - "tag": "electrophysiology" - }, - { - "id": 289, - "tag": "MIES" - }, - { - "id": 290, - "tag": "DANDI:000107" - }, - { - "id": 181, - "tag": "DANDI" - } - ], - "timestamp_created": "2023-02-16 10:12:49.231370+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000107/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "89": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.210914.1904", - "id": 89, - "name": "Patch-seq recordings from human cortex (June 2021)", - "repository_type": "dandi", - "summary": "Whole-cell Patch-seq recordings from neurons of the human neocortex from the Allen Institute for Brain Science, released in June 2021.", - "tags": [ - { - "id": 27, - "tag": "Patch-seq" - }, - { - "id": 7, - "tag": "human" - }, - { - "id": 40, - "tag": "neocortex" - }, - { - "id": 245, - "tag": "DANDI:000109" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 77, - "tag": "Human" - } - ], - "timestamp_created": "2023-02-16 10:12:50.648282+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000109/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "90": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.210914.1732", - "id": 90, - "name": "Gillespie et al (2021) Hippocampal replay reflects specific past experiences rather than a plan for subsequent choice", - "repository_type": "dandi", - "summary": "This dataset includes the electrophysiological (dorsal CA1 tetrodes) and behavioral (port triggers, reward delivery, and position tracking) data from Gillespie et al, Neuron 2021: \"Hippocampal replay reflects specific past experiences rather than a plan for subsequent choice\". For more information about this data, please contact Anna Gillespie or Loren Frank. ", - "tags": [ - { - "id": 246, - "tag": "DANDI:000115" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 247, - "tag": "Rat; norway rat; rats; brown rat" - } - ], - "timestamp_created": "2023-02-16 10:12:51.863462+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000115/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "91": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 91, - "name": "1U01MH116990-01_July_2021", - "repository_type": "dandi", - "summary": "Patch-clamp recording data from spinal cord neurons. Data was generated at Huizhong Tao's lab at USC(htao@usc.edu). The recording was performed by Can Tao(cantao@usc.edu) and Bo Peng(pengb@usc.edu).", - "tags": [ - { - "id": 95, - "tag": "spinal cord" - }, - { - "id": 96, - "tag": "patch-clamp" - }, - { - "id": 248, - "tag": "DANDI:000117" - }, - { - "id": 181, - "tag": "DANDI" - } - ], - "timestamp_created": "2023-02-16 10:12:53.099432+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000117/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "92": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 92, - "name": "Human fNIRS recordings of motor cortex during finger-tapping task", - "repository_type": "dandi", - "summary": "This experiment examines how the motor cortex is activated during a finger-tapping task. Participants are asked to either tap their left thumb to fingers, tap their right thumb to fingers, or no cue is given (control). Tapping lasts for 5 seconds and is prompted by an auditory cue. Sensors are placed over the motor cortex as described in the montage section in the link below, short channels are attached to the scalp too. Further details about the experiment (including presentation code) can be found at https://github.com/rob-luke/experiment-fNIRS-tapping.", - "tags": [ - { - "id": 97, - "tag": "fNIRS" - }, - { - "id": 98, - "tag": "Haemodynamics" - }, - { - "id": 99, - "tag": "Motor Cortex" - }, - { - "id": 100, - "tag": "Finger Tapping Task" - }, - { - "id": 249, - "tag": "DANDI:000122" - }, - { - "id": 181, - "tag": "DANDI" - } - ], - "timestamp_created": "2023-02-16 10:12:54.299101+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000122/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "93": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.210813.0327", - "id": 93, - "name": "NWB API Test Data", - "repository_type": "dandi", - "summary": "This dandiset consists of NWB files used for testing the NWB APIs (PyNWB, MatNWB).", - "tags": [ - { - "id": 250, - "tag": "DANDI:000126" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 10:12:55.542787+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000126/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "94": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.220113.0359", - "id": 94, - "name": "Area2_Bump: macaque somatosensory area 2 spiking activity during reaching with perturbations", - "repository_type": "dandi", - "summary": "This dataset contains sorted unit spiking times and behavioral data from a macaque performing a reaching task with perturbations. In the experimental task, the subject performed delayed center-out reaches using a manipulandum to control a cursor. On a portion of the trials, the manipulandum applied a bump during the center hold prior to the reach. Neural activity was recorded from an electrode array implanted in somatosensory area 2. Hand position, cursor position, force applied to the manipulandum, length and velocity of various arm muscles, and angle and velocity of various arm joints were all recorded during the experiment. Provided as part of the Neural Latents Benchmark: https://neurallatents.github.io.", - "tags": [ - { - "id": 101, - "tag": "Neural Latents Benchmark" - }, - { - "id": 102, - "tag": "NLB" - }, - { - "id": 251, - "tag": "DANDI:000127" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 242, - "tag": "Rhesus monkey" - } - ], - "timestamp_created": "2023-02-16 10:12:56.798832+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000127/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "95": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.220113.0400", - "id": 95, - "name": "MC_Maze: macaque primary motor and dorsal premotor cortex spiking activity during delayed reaching", - "repository_type": "dandi", - "summary": "This dataset contains sorted unit spiking times and behavioral data from a macaque performing a delayed reaching task. The experimental task was a center-out reaching task with obstructing barriers forming a maze, resulting in a variety of straight and curved reaches. Neural activity was recorded from electrode arrays implanted in the motor cortex (M1) and dorsal premotor cortex (PMd). Cursor position, hand position, and eye position were also recorded during the experiment, and hand velocity was calculated offline from hand position. Provided as part of the Neural Latents Benchmark: https://neurallatents.github.io.", - "tags": [ - { - "id": 101, - "tag": "Neural Latents Benchmark" - }, - { - "id": 102, - "tag": "NLB" - }, - { - "id": 252, - "tag": "DANDI:000128" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 242, - "tag": "Rhesus monkey" - } - ], - "timestamp_created": "2023-02-16 10:12:58.014063+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000128/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "96": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.241017.1444", - "id": 96, - "name": "MC_RTT: macaque motor cortex spiking activity during self-paced reaching", - "repository_type": "dandi", - "summary": "This dataset contains sorted unit spiking times and behavioral data from a macaque performing a self-paced reaching task. In the experimental task, the subject reached between targets randomly selected from an 8x8 grid without gaps or pre-movement delay intervals. Neural activity was recorded from an electrode array implanted in the primary motor cortex. Finger position, cursor position, and target position were also recorded during the experiment. Provided as part of the Neural Latents Benchmark: https://neurallatents.github.io.", - "tags": [ - { - "id": 101, - "tag": "Neural Latents Benchmark" - }, - { - "id": 102, - "tag": "NLB" - }, - { - "id": 253, - "tag": "DANDI:000129" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 242, - "tag": "Rhesus monkey" - } - ], - "timestamp_created": "2023-02-16 10:12:59.185028+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000129/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "97": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.241017.1448", - "id": 97, - "name": "DMFC_RSG: macaque dorsomedial frontal cortex spiking activity during time interval reproduction task", - "repository_type": "dandi", - "summary": "This dataset contains sorted unit spiking times from a macaque performing a time-interval reproduction task. In the experimental task, the monkey was presented with two stimuli separated by a specific interval of time. The monkey then attempted to time their response such that the interval between the second stimulus and their response matched the interval separating the two stimuli. Neural activity was recorded from neural probes implanted in the dorsomedial frontal cortex. Provided as part of the Neural Latents Benchmark: https://neurallatents.github.io.", - "tags": [ - { - "id": 101, - "tag": "Neural Latents Benchmark" - }, - { - "id": 102, - "tag": "NLB" - }, - { - "id": 254, - "tag": "DANDI:000130" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 242, - "tag": "Rhesus monkey" - } - ], - "timestamp_created": "2023-02-16 10:13:00.393194+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000130/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "98": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.220113.0407", - "id": 98, - "name": "MC_Maze_Large: macaque primary motor and dorsal premotor cortex spiking activity during delayed reaching", - "repository_type": "dandi", - "summary": "This dataset contains sorted unit spiking times and behavioral data from a macaque performing a delayed reaching task. The experimental task was a center-out reaching task with obstructing barriers forming a maze, resulting in a variety of straight and curved reaches. Neural activity was recorded from electrode arrays implanted in the motor cortex (M1) and dorsal premotor cortex (PMd). Cursor position, hand position, and eye position were also recorded during the experiment, and hand velocity was calculated offline from hand position. The provided data has been limited to 500 train trials and 100 test trials. Provided as part of the Neural Latents Benchmark: https://neurallatents.github.io.", - "tags": [ - { - "id": 101, - "tag": "Neural Latents Benchmark" - }, - { - "id": 102, - "tag": "NLB" - }, - { - "id": 255, - "tag": "DANDI:000138" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 242, - "tag": "Rhesus monkey" - } - ], - "timestamp_created": "2023-02-16 10:13:01.624413+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000138/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "99": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.220113.0408", - "id": 99, - "name": "MC_Maze_Medium: macaque primary motor and dorsal premotor cortex spiking activity during delayed reaching", - "repository_type": "dandi", - "summary": "This dataset contains sorted unit spiking times and behavioral data from a macaque performing a delayed reaching task. The experimental task was a center-out reaching task with obstructing barriers forming a maze, resulting in a variety of straight and curved reaches. Neural activity was recorded from electrode arrays implanted in the motor cortex (M1) and dorsal premotor cortex (PMd). Cursor position, hand position, and eye position were also recorded during the experiment, and hand velocity was calculated offline from hand position. The provided data has been limited to 250 train trials and 100 test trials. Provided as part of the Neural Latents Benchmark: https://neurallatents.github.io.", - "tags": [ - { - "id": 101, - "tag": "Neural Latents Benchmark" - }, - { - "id": 102, - "tag": "NLB" - }, - { - "id": 256, - "tag": "DANDI:000139" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 242, - "tag": "Rhesus monkey" - } - ], - "timestamp_created": "2023-02-16 10:13:02.933264+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000139/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "100": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.220113.0408", - "id": 100, - "name": "MC_Maze_Small: macaque primary motor and dorsal premotor cortex spiking activity during delayed reaching", - "repository_type": "dandi", - "summary": "This dataset contains sorted unit spiking times and behavioral data from a macaque performing a delayed reaching task. The experimental task was a center-out reaching task with obstructing barriers forming a maze, resulting in a variety of straight and curved reaches. Neural activity was recorded from electrode arrays implanted in the motor cortex (M1) and dorsal premotor cortex (PMd). Cursor position, hand position, and eye position were also recorded during the experiment, and hand velocity was calculated offline from hand position. The provided data has been limited to 100 train trials and 100 test trials. Provided as part of the Neural Latents Benchmark: https://neurallatents.github.io.", - "tags": [ - { - "id": 101, - "tag": "Neural Latents Benchmark" - }, - { - "id": 102, - "tag": "NLB" - }, - { - "id": 257, - "tag": "DANDI:000140" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 242, - "tag": "Rhesus monkey" - } - ], - "timestamp_created": "2023-02-16 10:13:04.230012+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000140/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "101": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.211007.1926", - "id": 101, - "name": "20210923_AIBS_Patchseq_human", - "repository_type": "dandi", - "summary": "U01 Lein PatchSeq upload September 2021", - "tags": [ - { - "id": 27, - "tag": "Patch-seq" - }, - { - "id": 7, - "tag": "human" - }, - { - "id": 103, - "tag": "neocortical" - }, - { - "id": 258, - "tag": "DANDI:000142" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 77, - "tag": "Human" - } - ], - "timestamp_created": "2023-02-16 10:13:05.609810+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000142/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "102": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 102, - "name": "IHC Validation Data", - "repository_type": "dandi", - "summary": "Confocal images of constained CDI and commercially available antibodies to the same target in mouse and human brain tissues", - "tags": [ - { - "id": 259, - "tag": "DANDI:000143" - }, - { - "id": 181, - "tag": "DANDI" - } - ], - "timestamp_created": "2023-02-16 10:13:06.844543+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000143/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "103": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 103, - "name": "croat-test", - "repository_type": "dandi", - "summary": "Testing", - "tags": [ - { - "id": 260, - "tag": "DANDI:000144" - }, - { - "id": 181, - "tag": "DANDI" - } - ], - "timestamp_created": "2023-02-16 10:13:08.004199+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000144/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "104": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.221122.2256", - "id": 104, - "name": "PPC_Finger: human posterior parietal cortex recordings during attempted finger movements", - "repository_type": "dandi", - "summary": "This dataset contains neural activity and trial information from a tetraplegic human participant performing a brain-computer interface (BCI) finger press task. Neural activity was recorded from a 96-channel Utah array (Blackrock) implanted in the left posterior parietal cortex (PPC) at the junction of the postcentral and intraparietal sulci (PC-IP). On each trial, a finger cue, at a pseudorandom location on a screen, was selected. The participant immediately looked at the cue and pressed the cued finger until decoder feedback was shown (1.5 seconds after the cue).\n\nThis dataset includes sorted unit spiking times, finger cue, in-session classified finger, cue location, and trial timing.", - "tags": [ - { - "id": 104, - "tag": "PPC" - }, - { - "id": 7, - "tag": "human" - }, - { - "id": 105, - "tag": "finger" - }, - { - "id": 261, - "tag": "DANDI:000147" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 262, - "tag": "Homo sapiens - Human" - } - ], - "timestamp_created": "2023-02-16 10:13:09.206885+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000147/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "105": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 105, - "name": "Electrophysiological properties of adult mouse spinal cord neurons - 01_Oct_2021", - "repository_type": "dandi", - "summary": "Patch-clamp recording data from spinal cord neurons. Data was generated at Huizhong Tao's lab at USC(htao@usc.edu). The recording was performed by Can Tao(cantao@usc.edu) and Bo Peng(pengb@usc.edu).", - "tags": [ - { - "id": 263, - "tag": "DANDI:000148" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-02-16 10:13:10.387576+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000148/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "106": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 106, - "name": "IBL ephys data", - "repository_type": "dandi", - "summary": "This dataset is a result of a multi-institution, cross country collaboration of labs, called International Brain Laboratory conducting standardized experiments on decision-making in mice. This dataset contains contains data similar to the dandiset https://dandiarchive.org/dandiset/000045 with additional ephys data.", - "tags": [ - { - "id": 264, - "tag": "DANDI:000149" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 10:13:11.568922+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000149/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "107": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.211118.1526", - "id": 107, - "name": "Aery Jones et al (2021) Dentate Gyrus and CA3 GABAergic Interneurons Bidirectionally Modulate Signatures of Internal and External Drive to CA1", - "repository_type": "dandi", - "summary": "The experiment consisted of three cohorts of PVCre, SSTCre, and PVCre/SSTCre mice injected with either AAV5-hSyn-DIO-hM4D(Gi)-mCherry (hM4D) or AAV5-hSyn-DIO-mCherry (empty vector) in DG and CA3, then injected with vehicle or CNO prior to recordings. Recordings from all layers of CA1, CA3, and DG were taken during home cage rest and linear track movement. Data are described in Aery Jones et al, 2021, bioRxiv: \"Dentate Gyrus and CA3 GABAergic Interneurons Bidirectionally Modulate Signatures of Internal and External Drive to CA1\". For more information about this data, please contact Emily Aery Jones, Yadong Huang, or Loren Frank.", - "tags": [ - { - "id": 92, - "tag": "hippocampus" - }, - { - "id": 29, - "tag": "mouse" - }, - { - "id": 106, - "tag": "LFP" - }, - { - "id": 265, - "tag": "DANDI:000165" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 10:13:12.864708+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000165/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "108": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.250624.0434", - "id": 108, - "name": "Layer-Specific Physiological Features and Interlaminar Interactions in the Primary Visual Cortex of the Mouse", - "repository_type": "dandi", - "summary": "The relationship between mesoscopic local field potentials (LFPs) and single-neuron firing in the multi-layered neocortex is poorly understood. Simultaneous recordings from all layers in the primary visual cortex (V1) of the behaving mouse revealed functionally defined layers in V1. The depth of maximum spike power and sink-source distributions of LFPs provided consistent laminar landmarks across animals. Coherence of gamma oscillations (30-100 Hz) and spike-LFP coupling identified six physiological layers and further sublayers. Firing rates, burstiness, and other electrophysiological features of neurons displayed unique layer and brain state dependence. Spike transmission strength from layer 2/3 cells to layer 5 pyramidal cells and interneurons was stronger during waking compared with non-REM sleep but stronger during non-REM sleep among deep-layer excitatory neurons. A subset of deep-layer neurons was active exclusively in the DOWN state of non-REM sleep. These results bridge mesoscopic LFPs and single-neuron interactions with laminar structure in V1.", - "tags": [ - { - "id": 12, - "tag": "current source density" - }, - { - "id": 13, - "tag": "laminar recordings" - }, - { - "id": 8, - "tag": "cortex" - }, - { - "id": 35, - "tag": "electrophysiology" - }, - { - "id": 266, - "tag": "DANDI:000166" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 10:13:14.155445+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000166/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "109": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230720.2001", - "id": 109, - "name": "Two photon calcium imaging of mice piriform cortex under passive odor presentation", - "repository_type": "dandi", - "summary": "Two-photon calcium imaging of mouse piriform cortex pyramidal population, under head-fixed condition and passive odor stimulus delivery. There were 10 different odors used (8 trials each), delivered at 10-second for each of the 30-second trial. Imaging was preprocessed with Suite2p. The data was collected by Simon Daste in the Fleischmann lab at Brown University. Showcase notebook available at: https://gitlab.com/fleischmann-lab/datasets/daste-odor-set-2021-11", - "tags": [ - { - "id": 267, - "tag": "DANDI:000167" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-02-16 10:13:15.544762+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000167/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "110": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 110, - "name": "Simultaneous loose seal cell-attached recordings and two-photon imaging of GCaMP8 expressing mouse V1 neurons with drifting gratings visual stimuli", - "repository_type": "dandi", - "summary": "We tested the jGCaMP8 sensors in L2/3 pyramidal neurons of mouse primary visual cortex. We made a craniotomy over V1 and infected neurons with adeno-associated virus (AAV2/1-hSynapsin-1) encoding jGCaMP8 variants (s/m/f), jGCaMP7f, or XCaMP-Gf. 18-80 days after the virus injection, the mouse was anesthetized, and we surgically removed the cranial window and performed durotomy. The craniotomy was filled with 10-15 \u03bcL of 1.5% agarose, and a D-shaped coverslip was secured on top to suppress brain motion and leave access to the brain on the lateral side of the craniotomy. Then mice were lightly anesthetized and mounted under a custom two-photon microscope. Full-field, high-contrast drifting gratings were presented in each of eight directions to the contralateral eye. Two-photon imaging (122 Hz) was performed of L2/3 somata and neuropil combined with loose-seal, cell-attached electrophysiological recording of a single neuron in the field of view.", - "tags": [ - { - "id": 107, - "tag": "2-photon" - }, - { - "id": 33, - "tag": "visual cortex" - }, - { - "id": 108, - "tag": "calcium" - }, - { - "id": 109, - "tag": "spike" - }, - { - "id": 110, - "tag": "action potential" - }, - { - "id": 111, - "tag": "layer 2" - }, - { - "id": 112, - "tag": "AAV" - }, - { - "id": 113, - "tag": "adeno-associated virus" - }, - { - "id": 114, - "tag": "jGCaMP8s" - }, - { - "id": 115, - "tag": "jGCaMP8m" - }, - { - "id": 116, - "tag": "jGCaMP8f" - }, - { - "id": 117, - "tag": "jGCaMP7f" - }, - { - "id": 118, - "tag": "XCaMP-Gf" - }, - { - "id": 268, - "tag": "DANDI:000168" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-02-16 10:13:16.808419+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000168/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "111": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.220927.0404", - "id": 111, - "name": "Neural Spiking Data in Primary Somatosensory Cortex before and after Transcranial Focused Ultrasound Stimulation in Rats", - "repository_type": "dandi", - "summary": "In this study, we investigate how transcranial focused ultrasound (tFUS) modulates neural interaction and response to peripheral electrical limb stimulation through intracranial multi-electrode recordings in the rat somatosensory cortex. Rats were anesthetized using isoflurane anesthesia at 2%. Recordings were taken while delivering peripheral electrical stimulation once every 5 seconds to analyze the neural response in the rat S1HL region, before and after ultrasound. We delivered ultrasound for 5 minutes in a pulsed pattern at 5 different stimulation repetition frequencies (10 Hz, 50 Hz, 75 Hz, 100 Hz, and 125 Hz) to induce frequency dependent plasticity in a manner similar to that found following tetanic electrical stimulation. The fundamental frequency of ultrasound used was 0.5 MHz, the pulse repetition frequency (PRF) was 3 kHz, and the total duty cycle was 36%. This dataset includes spike timing data from the recordings including time before and after ultrasound stimulation, for both true ultrasound conditions and sham ultrasound conditions in which ultrasound is directed off target. Details of the study are in the following publication: S. Ramachandran, X. Niu, K. Yu, B. He, \"Transcranial ultrasound neuromodulation induces neuronal correlation change in the rat somatosensory cortex\", 2022 J. Neural Eng. 19 056002 https://pubmed.ncbi.nlm.nih.gov/35947970/. Please cite this publication if you would use a portion of the data. ", - "tags": [ - { - "id": 119, - "tag": "Ultrasound" - }, - { - "id": 120, - "tag": "Plasticity" - }, - { - "id": 121, - "tag": "Rat" - }, - { - "id": 122, - "tag": "tFUS" - }, - { - "id": 123, - "tag": "Somatosensory Cortex" - }, - { - "id": 269, - "tag": "DANDI:000173" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 205, - "tag": "Rattus norvegicus - Norway rat" - } - ], - "timestamp_created": "2023-02-16 10:13:18.072407+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000173/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "112": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.220103.2119", - "id": 112, - "name": "Visual cortical activity in mice performing naturalistic virtual foraging task", - "repository_type": "dandi", - "summary": "Large FOV two-photon calcium imaging dataset recorded from V1 L2/3 neurons from mouse performing a naturalistic foraging task in virtual reality.", - "tags": [ - { - "id": 270, - "tag": "DANDI:000206" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-02-16 10:13:19.284994+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000206/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "113": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230530.1822", - "id": 113, - "name": "Data for: Neurons detect cognitive boundaries to structure episodic memories in humans (Zheng et al., 2022, Nat Neuro in press)", - "repository_type": "dandi", - "summary": "This dataset contains human single-neuron data recorded from the medial temporal lobe (MTL) during a set of experiments to explore the role of neurons that respond to cognitive boundaries. This dataset accompanies the paper cited below. Example code on how to plot this data can be found at https://github.com/rutishauserlab/cogboundary-zheng .\n\nReference (to be updated upon publication): Cognitive boundary signals in the human medial temporal lobe shape episodic memory representation. Jie Zheng, Andrea G\u00f3mez Palacio Schjetnan, Mar Yebra, Clayton Mosher, Suneil Kalia, Taufik A. Valiante, Adam N. Mamelak, Gabriel Kreiman, Ueli Rutishauser. bioRxiv 2021.01.16.426538. [Nat Neuro, in press, 2022]", - "tags": [ - { - "id": 124, - "tag": "human single neuron" - }, - { - "id": 92, - "tag": "hippocampus" - }, - { - "id": 125, - "tag": "episodic memory" - }, - { - "id": 126, - "tag": "event segmentation" - }, - { - "id": 127, - "tag": "amygdala" - }, - { - "id": 128, - "tag": "parahippocampal gyrus" - }, - { - "id": 129, - "tag": "cognitive boundaries" - }, - { - "id": 130, - "tag": "continuous experience" - }, - { - "id": 131, - "tag": "ROH consortium" - }, - { - "id": 271, - "tag": "DANDI:000207" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 262, - "tag": "Homo sapiens - Human" - } - ], - "timestamp_created": "2023-02-16 10:13:20.567204+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000207/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "114": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230522.1918", - "id": 114, - "name": "20211223_AIBS_Patchseq_human", - "repository_type": "dandi", - "summary": "U01 Lein PatchSeq upload December 2021", - "tags": [ - { - "id": 27, - "tag": "Patch-seq" - }, - { - "id": 7, - "tag": "human" - }, - { - "id": 103, - "tag": "neocortical" - }, - { - "id": 272, - "tag": "DANDI:000209" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 77, - "tag": "Human" - } - ], - "timestamp_created": "2023-02-16 10:13:21.773180+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000209/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "115": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 115, - "name": "Tracking of Drosophila during egg-laying decisions", - "repository_type": "dandi", - "summary": "Dataset on the egg-laying behavior of flies used to understand how flies make egg-laying decisions. Each NWB file contains time series data from an individual fly: the x-y position of the fly, egg-deposition moments of the fly, and sucrose concentration underneath the fly. A variety of fly genotypes and a variety of environments (egg-laying chambers) were used. Data and methods are described in \"A rise-to-threshold signal for a relative value deliberation\" (https://www.biorxiv.org/content/10.1101/2021.09.23.461548v1) and \u201cAn internal expectation guides Drosophila egg-laying decisions\u201d (https://doi.org/10.1126/sciadv.abn3852). Please contact Vikram Vijayan and/or Gaby Maimon for more information including different download options and different raw/processed data formats.", - "tags": [ - { - "id": 132, - "tag": "Drosophila" - }, - { - "id": 133, - "tag": "egg laying" - }, - { - "id": 134, - "tag": "flies" - }, - { - "id": 20, - "tag": "decision making" - }, - { - "id": 135, - "tag": "internal expectation" - }, - { - "id": 273, - "tag": "DANDI:000212" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 274, - "tag": "Drosophila melanogaster - Fruit fly" - } - ], - "timestamp_created": "2023-02-16 10:13:23.122559+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000212/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "116": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.250624.0438", - "id": 116, - "name": "Transformation of a Spatial Map across the Hippocampal-Lateral Septal Circuit", - "repository_type": "dandi", - "summary": "The hippocampus constructs a map of the environment. How this \u201ccognitive map\u201d is utilized by other brain regions to guide behavior remains unexplored. To examine how neuronal firing patterns in the hippocampus are transmitted and transformed, we recorded neurons in its principal subcortical target, the lateral septum (LS). We observed that LS neurons carry reliable spatial information in the phase of action potentials, relative to hippocampal theta oscillations, while the firing rates of LS neurons remained uninformative. Furthermore, this spatial phase code had an anatomical microstructure within the LS and was bound to the hippocampal spatial code by synchronous gamma frequency cell assemblies. Using a data-driven model, we show that rate-independent spatial tuning arises through the dynamic weighting of CA1 and CA3 cell assemblies. Our findings demonstrate that transformation of the hippocampal spatial map depends on higher-order theta-dependent neuronal sequences.", - "tags": [ - { - "id": 92, - "tag": "hippocampus" - }, - { - "id": 136, - "tag": "lateral septum" - }, - { - "id": 35, - "tag": "electrophysiology" - }, - { - "id": 275, - "tag": "DANDI:000213" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 205, - "tag": "Rattus norvegicus - Norway rat" - } - ], - "timestamp_created": "2023-02-16 10:13:24.372506+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000213/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "117": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.220125.2004", - "id": 117, - "name": "Sniff-synchronized, gradient-guided olfactory search by freely moving mice -- Behavioral Dataset", - "repository_type": "dandi", - "summary": "This dataset contains the movement tracking, sniff recording, and trial statistics for the dataset used in the publication: Sniff-synchronized, gradient-guided olfactory search by freely moving mice in eLife (Findley et al. 2021)", - "tags": [ - { - "id": 276, - "tag": "DANDI:000217" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-02-16 10:13:25.912515+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000217/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "118": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.250624.0439", - "id": 118, - "name": "Routing of Hippocampal Ripples to Subcortical Structures via the Lateral Septum", - "repository_type": "dandi", - "summary": "The mnemonic functions of hippocampal sharp wave ripples (SPW-Rs) have been studied extensively. Because hippocampal outputs affect not only cortical but also subcortical targets, we examined the impact of SPW-Rs on the firing patterns of lateral septal (LS) neurons in behaving rats. A large fraction of SPW-Rs were temporally locked to high-frequency oscillations (HFOs) (120\u2013180 Hz) in LS, with strongest coupling during non-rapid eye movement (NREM) sleep, followed by waking immobility. However, coherence and spike-local field potential (LFP) coupling between the two structures were low, suggesting that HFOs are generated locally within the LS GABAergic population. This hypothesis was supported by optogenetic induction of HFOs in LS. Spiking of LS neurons was largely independent of the sequential order of spiking in SPW-Rs but instead correlated with the magnitude of excitatory synchrony of the hippocampal output. Thus, LS is strongly activated by SPW-Rs and may convey hippocampal population events to its hypothalamic and brainstem targets.", - "tags": [ - { - "id": 92, - "tag": "hippocampus" - }, - { - "id": 136, - "tag": "lateral septum" - }, - { - "id": 137, - "tag": "electrophyisology" - }, - { - "id": 277, - "tag": "DANDI:000218" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 205, - "tag": "Rattus norvegicus - Norway rat" - } - ], - "timestamp_created": "2023-02-16 10:13:27.133551+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000218/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "119": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 119, - "name": "Two photon calcium imaging in the CA1 region of the hippocampus in neonatal mice.", - "repository_type": "dandi", - "summary": "We performed in vivo 2-photon calcium imaging in the CA1 region of the hippocampus in awake mouse pups aged between 5 and 12 days postnatal. We used GCaMP6s calcium indicator in WT mice or GCaMP6s and flex-tdTomato in GadCre mice to record calcium dynamics from both pyramidal cells and interneurons. The imaging data set was acquired at 8 Hz, in field of view of 400x400 \u00b5m. Simultaneously with imaging, we record the spontaneous motor behavior of the mouse pups.", - "tags": [ - { - "id": 278, - "tag": "DANDI:000219" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-02-16 10:13:28.767193+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000219/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "120": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 120, - "name": "Electrophysiological properties of adult mouse spinal cord neurons - 25_Jan_2022", - "repository_type": "dandi", - "summary": "Patch-clamp recording data from spinal cord neurons. Data was generated at Huizhong Tao's lab at USC(htao@usc.edu). The recording was performed by Can Tao(cantao@usc.edu) and Bo Peng(pengb@usc.edu).", - "tags": [ - { - "id": 279, - "tag": "DANDI:000220" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-02-16 10:13:29.933785+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000220/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "121": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.220307.1320", - "id": 121, - "name": "A midbrain-thalamus-cortex circuit reorganizes cortical dynamics to initiate movement", - "repository_type": "dandi", - "summary": "Data from \"A midbrain-thalamus-cortex circuit reorganizes cortical dynamics to initiate movement\" Inagaki & Chen et al\nExample codes to plot data is at https://github.com/hidehikoinagaki/InagakiAndChenEtAl2022", - "tags": [ - { - "id": 138, - "tag": "Midbrain" - }, - { - "id": 139, - "tag": "ALM" - }, - { - "id": 140, - "tag": "motor planning" - }, - { - "id": 141, - "tag": "movement initiation" - }, - { - "id": 295, - "tag": "DANDI:000221" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-02-16 10:13:31.176465+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000221/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "122": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.220823.0826", - "id": 122, - "name": "Inferring monosynaptic connections from paired spine calcium imaging and large-scale recording of extracellular spiking", - "repository_type": "dandi", - "summary": "This dataset contains paired calcium imaging and high-density microelectrode array (HD-MEA) recordings from cortical embryonic cell cultures. \nThe data is used to infer monosynaptic connections using dendritic spine calcium traces and extracellular spiking.\nEach file includes:\n- raw extracellular recordings \n- spike-sorted units\n- imaging series\n- segmentation ROIs (of the target spines and adjacent dendritic shaft)", - "tags": [ - { - "id": 142, - "tag": "calcium imaging; extracellular recordings; HD-MEA; spike sorting; dendritic spines" - }, - { - "id": 296, - "tag": "DANDI:000223" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 205, - "tag": "Rattus norvegicus - Norway rat" - } - ], - "timestamp_created": "2023-02-16 10:13:32.382113+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000223/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "123": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230607.1747", - "id": 123, - "name": "Active Touch and Self-Motion Encoding by Merkel Cell-Associated Afferents", - "repository_type": "dandi", - "summary": "Touch perception depends on integrating signals from multiple types of peripheral mechanoreceptors. Merkel-cell associated afferents are thought to play a major role in form perception by encoding surface features of touched objects. However, activity of Merkel afferents during active touch has not been directly measured. Here, we show that Merkel and unidentified slowly adapting afferents in the whisker system of behaving mice respond to both self-motion and active touch. Touch responses were dominated by sensitivity to bending moment (torque) at the base of the whisker and its rate of change and largely explained by a simple mechanical model. Self-motion responses encoded whisker position within a whisk cycle (phase), not absolute whisker angle, and arose from stresses reflecting whisker inertia and activity of specific muscles. Thus, Merkel afferents send to the brain multiplexed information about whisker position and surface features, suggesting that proprioception and touch converge at the earliest neural level.", - "tags": [ - { - "id": 297, - "tag": "DANDI:000226" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-02-16 10:13:33.628682+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000226/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "124": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 124, - "name": "20220330_AIBS_Patchseq_human", - "repository_type": "dandi", - "summary": "U01 Lein PatchSeq upload March 2022", - "tags": [ - { - "id": 27, - "tag": "Patch-seq" - }, - { - "id": 7, - "tag": "human" - }, - { - "id": 103, - "tag": "neocortical" - }, - { - "id": 298, - "tag": "DANDI:000228" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 262, - "tag": "Homo sapiens - Human" - } - ], - "timestamp_created": "2023-02-16 10:13:34.848105+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000228/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "141": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.220805.1724", - "id": 141, - "name": " update 1- test release 0.7.0 - Drosophila visual neural responses to stochastic stimuli ", - "repository_type": "dandi", - "summary": "Associated Reference Publication: Excitatory and inhibitory neural dynamics jointly tune motion detection\nNeurons integrate excitatory and inhibitory signals to produce their outputs, but the role of input timing in this integration remains poorly understood. Motion detection is a paradigmatic example of this integration, since theories of motion detection rely on different delays in visual signals. These delays allow circuits to compare scenes at different times to calculate the direction and speed of motion. Different motion detection circuits have different velocity sensitivity, but it remains untested how the response dynamics of individual cell types drive this tuning. Here, we sped up or slowed down specific neuron types in Drosophila\u2019s motion detection circuit by manipulating ion channel expression. Altering the dynamics of individual neuron types upstream of motion detectors increased their sensitivity to fast or slow visual motion, exposing distinct roles for excitatory and inhibitory dynamics in tuning directional signals, including a role for the amacrine cell CT1. A circuit model constrained by functional data and anatomy qualitatively reproduced the observed tuning changes. Overall, these results reveal how excitatory and inhibitory dynamics together tune a canonical circuit computation.", - "tags": [ - { - "id": 1, - "tag": "tutorial" - } - ], - "timestamp_created": "2023-03-02 15:37:50.349608+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000296", - "user": { - "email": "simao@metacell.us", - "first_name": "Sim\u00e3o", - "id": "ee8a31d7-d54d-413c-a4c9-e140cf77404f", - "last_name": "Bolota de Couto Sa", - "username": "simao-osb" - }, - "user_id": "ee8a31d7-d54d-413c-a4c9-e140cf77404f" - }, - "143": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 143, - "name": "NeuroMLlite Showcase", - "repository_type": "github", - "summary": "Work in progress...", - "tags": [ - { - "id": 610, - "tag": "Generic" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 604, - "tag": "Network" - }, - { - "id": 612, - "tag": "NeuroML2" - }, - { - "id": 540, - "tag": "OSBv1" - } - ], - "timestamp_created": "2023-03-31 13:52:10.868638+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/NeuroML/NeuroMLlite", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "144": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.220805.1724", - "id": 144, - "name": "Drosophila visual neural responses to stochastic stimuli", - "repository_type": "dandi", - "summary": "Associated Reference Publication: Excitatory and inhibitory neural dynamics jointly tune motion detection\nNeurons integrate excitatory and inhibitory signals to produce their outputs, but the role of input timing in this integration remains poorly understood. Motion detection is a paradigmatic example of this integration, since theories of motion detection rely on different delays in visual signals. These delays allow circuits to compare scenes at different times to calculate the direction and speed of motion. Different motion detection circuits have different velocity sensitivity, but it remains untested how the response dynamics of individual cell types drive this tuning. Here, we sped up or slowed down specific neuron types in Drosophila\u2019s motion detection circuit by manipulating ion channel expression. Altering the dynamics of individual neuron types upstream of motion detectors increased their sensitivity to fast or slow visual motion, exposing distinct roles for excitatory and inhibitory dynamics in tuning directional signals, including a role for the amacrine cell CT1. A circuit model constrained by functional data and anatomy qualitatively reproduced the observed tuning changes. Overall, these results reveal how excitatory and inhibitory dynamics together tune a canonical circuit computation.", - "tags": [ - { - "id": 329, - "tag": "DANDI:000296" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 274, - "tag": "Drosophila melanogaster - Fruit fly" - } - ], - "timestamp_created": "2023-04-13 17:21:22.563580+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000296/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "145": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 145, - "name": "NEURON course", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 1, - "tag": "tutorial" - } - ], - "timestamp_created": "2023-05-12 08:20:09.996739+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/mcdougallab/neuron-course-june-2022", - "user": { - "email": "p.gl.eeson@gmail.com", - "first_name": "Padraig6", - "id": "b611c83e-483f-4b8c-a5c9-32ce5de9990f", - "last_name": "Gleeson", - "username": "padraig6" - }, - "user_id": "b611c83e-483f-4b8c-a5c9-32ce5de9990f" - }, - "147": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.240822.1759", - "id": 147, - "name": "Glia Accumulate Evidence that Actions Are Futile and Suppress Unsuccessful Behavior", - "repository_type": "dandi", - "summary": "When a behavior repeatedly fails to achieve its goal, animals often give up and become passive, which can be strategic for preserving energy or regrouping between attempts. It is unknown how the brain identifies behavioral failures and mediates this behavioral-state switch. In larval zebrafish swimming in virtual reality, visual feedback can be withheld so that swim attempts fail to trigger expected visual flow. After tens of seconds of such motor futility, animals became passive for similar durations. Whole-brain calcium imaging revealed noradrenergic neurons that responded specifically to failed swim attempts and radial astrocytes whose calcium levels accumulated with increasing numbers of failed attempts. Using cell ablation and optogenetic or chemogenetic activation, we found that noradrenergic neurons progressively activated brainstem radial astrocytes, which then suppressed swimming. Thus, radial astrocytes perform a computation critical for behavior: they accumulate evidence that current actions are ineffective and consequently drive changes in behavioral states.", - "tags": [ - { - "id": 159, - "tag": "neuroscience" - }, - { - "id": 160, - "tag": "glia" - }, - { - "id": 161, - "tag": "astrocytes" - }, - { - "id": 162, - "tag": "norepinephrine" - }, - { - "id": 163, - "tag": "noradrenaline" - }, - { - "id": 164, - "tag": "learned helplessness" - }, - { - "id": 165, - "tag": "neuromodulation" - }, - { - "id": 166, - "tag": "behavioral states" - }, - { - "id": 167, - "tag": "evidence accumulation" - }, - { - "id": 168, - "tag": "zebrafish" - }, - { - "id": 182, - "tag": "DANDI:000350" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 305, - "tag": "Danio rerio - Zebra fish" - } - ], - "timestamp_created": "2023-11-27 17:44:17.412979+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000350/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "148": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 148, - "name": "Jeong et al (2022) Mesolimbic dopamine release conveys causal associations", - "repository_type": "dandi", - "summary": "This dataset includes fiber photometry (NAcc) and behavioral data from Jeong et al., 2022: \"Mesolimbic dopamine release conveys causal associations\". Animals names and session numbers used for each figure can be found from 'Subject and session information' in Related resource.", - "tags": [ - { - "id": 184, - "tag": "DANDI:000351" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-11-27 17:44:18.983514+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000351/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "149": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 149, - "name": "Simultaneous loose seal cell-attached recordings and two-photon imaging of GCaMP8 expressing mouse V1 neurons with drifting gratings visual stimuli - RAW movies", - "repository_type": "dandi", - "summary": "We tested the jGCaMP8 sensors in L2/3 pyramidal neurons of mouse primary visual cortex. We made a craniotomy over V1 and infected neurons with adeno-associated virus (AAV2/1-hSynapsin-1) encoding jGCaMP8 variants (s/m/f), jGCaMP7f, or XCaMP-Gf. 18-80 days after the virus injection, the mouse was anesthetized, and we surgically removed the cranial window and performed durotomy. The craniotomy was filled with 10-15 \u03bcL of 1.5% agarose, and a D-shaped coverslip was secured on top to suppress brain motion and leave access to the brain on the lateral side of the craniotomy. Then mice were lightly anesthetized and mounted under a custom two-photon microscope. Full-field, high-contrast drifting gratings were presented in each of eight directions to the contralateral eye. Two-photon imaging (122 Hz) was performed of L2/3 somata and neuropil combined with loose-seal, cell-attached electrophysiological recording of a single neuron in the field of view. \nThis dataset contains the raw 2-photon videos, for registered movies see: https://dandiarchive.org/dandiset/000168/", - "tags": [ - { - "id": 348, - "tag": "DANDI:000362" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-11-27 17:44:20.334477+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000362/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "150": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.231012.2129", - "id": 150, - "name": "Mesoscale Activity Map Dataset", - "repository_type": "dandi", - "summary": "Mesoscale Activity Map Project. Map behavior-related activity in a multi-regional network supporting memory-guided movement in mice. Anatomy-guided recordings from multiple connected brain regions, from anterior lateral motor cortex to the medulla.\n\nSupported by Simons Collaboration on the Global Brain, Janelia Visitor Project, NIH U19NS123714-01, R01NS112312, R01EB028171, McKnight foundation", - "tags": [ - { - "id": 349, - "tag": "DANDI:000363" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-11-27 17:44:21.684493+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000363/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "151": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 151, - "name": "Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex", - "repository_type": "dandi", - "summary": "Recent advances in multi-electrode array technology have made it possible to monitor large neuronal ensembles at cellular resolution in animal models. In humans, however, current approaches restrict recordings to few neurons per penetrating electrode or combine the signals of thousands of neurons in local field potential (LFP) recordings. Here, we describe a new probe variant and set of techniques which enable simultaneous recording from over 200 well-isolated cortical single units in human participants during intraoperative neurosurgical procedures using silicon Neuropixels probes. We characterized a diversity of extracellular waveforms with eight separable single unit classes, with differing firing rates, locations along the length of the electrode array, waveform spatial spread, and modulation by LFP events such as inter-ictal discharges and burst suppression. While some challenges remain in creating a turn-key recording system, high-density silicon arrays provide a path for studying human-specific cognitive processes and their dysfunction at unprecedented spatiotemporal resolution. ", - "tags": [ - { - "id": 350, - "tag": "DANDI:000397" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 262, - "tag": "Homo sapiens - Human" - } - ], - "timestamp_created": "2023-11-27 17:44:22.854106+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000397/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "152": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 152, - "name": "LFPy", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 176, - "tag": "ECoG" - }, - { - "id": 177, - "tag": "MEG" - }, - { - "id": 106, - "tag": "LFP" - } - ], - "timestamp_created": "2023-12-05 14:00:39.554121+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/LFPy/LFPy", - "user": { - "email": "p.gleeson@gmail.com", - "first_name": "Padraig", - "id": "7089f659-90ad-4ed9-9715-2327f7e2e72f", - "last_name": "Gleeson Admin", - "username": "pgleeson" - }, - "user_id": "7089f659-90ad-4ed9-9715-2327f7e2e72f" - }, - "153": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "develop", - "id": 153, - "name": "CH", - "repository_type": "github", - "summary": "", - "tags": [], - "timestamp_created": "2023-12-11 06:52:25.324253+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/MetaCell/cloud-harness", - "user": { - "email": "gopal@metacell.us", - "first_name": "D. Gopal ", - "id": "0db2937f-6534-434f-9e38-ff6ed1cbe395", - "last_name": "Krishna", - "username": "gopal" - }, - "user_id": "0db2937f-6534-434f-9e38-ff6ed1cbe395" - }, - "154": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "master", - "id": 154, - "name": "DRF-YASG", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 2, - "tag": "neuroml" - }, - { - "id": 1, - "tag": "tutorial" - }, - { - "id": 5, - "tag": "netpyne" - } - ], - "timestamp_created": "2023-12-11 07:02:24.820214+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/axnsan12/drf-yasg", - "user": { - "email": "gopal@metacell.us", - "first_name": "D. Gopal ", - "id": "0db2937f-6534-434f-9e38-ff6ed1cbe395", - "last_name": "Krishna", - "username": "gopal" - }, - "user_id": "0db2937f-6534-434f-9e38-ff6ed1cbe395" - }, - "155": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 155, - "name": "Multiphoton imaging in macaque visual cortex (preliminary data)", - "repository_type": "dandi", - "summary": "Two- and three-photon imaging from V1/V2 labeled with GCaMP6s. Grating stimuli (sf x dir).", - "tags": [ - { - "id": 186, - "tag": "DANDI:000347" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 347, - "tag": "Macaca nemestrina - Pig-tailed macaque" - } - ], - "timestamp_created": "2023-12-15 17:57:29.816642+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000347/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "156": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.240123.1806", - "id": 156, - "name": "Dataset of human-single neuron activity during a Sternberg working memory task.", - "repository_type": "dandi", - "summary": "We present a dataset of 1809 single neurons recorded from the human medial temporal lobe (amygdala and hippocampus) and medial frontal lobe (anterior cingulate cortex, pre-supplementary motor area, ventral medial prefrontal cortex) across 41 sessions from 21 patients that underwent intracranial monitoring for epileptic activity. Subjects first performed a screening task (907 neurons), based on which we identified images for which highly selective cells were present in the medial temporal lobe. Subjects then performed a working memory task (902 neurons), in which they were sequentially presented with 1-3 images, and following a maintenance period, were asked if a probe was identical to one of the currently maintained images. This Neural data without borders (NWB) formatted dataset includes spike times, extracellular spike waveforms, stimuli presented, behavior, electrode locations, and subject demographics. As validation, we replicate previous findings on the existence of concept cells and their persistent activity during working memory maintenance. This dataset provides a substantial amount of rare human single neuron recordings together with behavior, thereby enabling investigation of the neural mechanisms of working memory at the single-neuron level.\n\nThis code accompanies the following data descriptor: \n* Kyzar M, Kami\u0144ski J, Brzezicka A, Reed CM, Chung JM, Mamelak AN, Rutishauser U. Dataset of human-single neuron activity during a Sternberg working memory task. Sci Data. 2024 Jan 18;11(1):89. doi: 10.1038/s41597-024-02943-8. PMID: 38238342; PMCID: PMC10796636.\n [Link to Paper](https://pubmed.ncbi.nlm.nih.gov/38238342/)\n* Sample code to access and analyze this dataset has been provided: https://github.com/rutishauserlab/workingmem-release-NWB\n", - "tags": [ - { - "id": 18, - "tag": "cognitive neuroscience" - }, - { - "id": 19, - "tag": "data standardization" - }, - { - "id": 280, - "tag": "working memory" - }, - { - "id": 22, - "tag": "neurophysiology" - }, - { - "id": 23, - "tag": "neurosurgery" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 25, - "tag": "open source" - }, - { - "id": 26, - "tag": "single-neurons" - }, - { - "id": 281, - "tag": "DANDI:000469" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 262, - "tag": "Homo sapiens - Human" - } - ], - "timestamp_created": "2023-12-15 18:25:14.295269+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000469/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "157": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 157, - "name": "Test", - "repository_type": "dandi", - "summary": "Test data", - "tags": [ - { - "id": 282, - "tag": "DANDI:000470" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-15 18:30:57.250975+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000470/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "165": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.250624.0430", - "id": 165, - "name": "Internally organized mechanisms of the head direction sense", - "repository_type": "dandi", - "summary": "The head-direction (HD) system functions as a compass, with member neurons robustly increasing their firing rates when the animal's head points in a specific direction. HD neurons may be driven by peripheral sensors or, as computational models postulate, internally generated (attractor) mechanisms. We addressed the contributions of stimulus-driven and internally generated activity by recording ensembles of HD neurons in the antero-dorsal thalamic nucleus and the post-subiculum of mice by comparing their activity in various brain states. The temporal correlation structure of HD neurons was preserved during sleep, characterized by a 60\u00b0-wide correlated neuronal firing (activity packet), both within and across these two brain structures. During rapid eye movement sleep, the spontaneous drift of the activity packet was similar to that observed during waking and accelerated tenfold during slow-wave sleep. These findings demonstrate that peripheral inputs impinge on an internally organized network, which provides amplification and enhanced precision of the HD signal.", - "tags": [ - { - "id": 283, - "tag": "DANDI:000056" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 193, - "tag": "House mouse" - } - ], - "timestamp_created": "2023-12-18 13:22:46.591695+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000056/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "166": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 166, - "name": "Light sheet imaging of the human brain", - "repository_type": "dandi", - "summary": "This dataset contains images of 1 mm sections of human brain tissue showing nuclei, NeuN+ cells and blood vessels. Each tissue section was first SHIELD-processed for protein protection and delipidated to clear the tissue. The tissues were stained with YOYO1 (nuclei), anti-NeuN antibody (with Rhodamine Red-X secondary antibody) and Lectin (blood vessel) for 8 days in total and then optically cleared using ExPROTOS (a refractive matching solution). The sample was imaged using light sheet microscopy at a resolution of ~2.5 um x 3.6 um x 2.5 um. Each slab was imaged using multiple stacks. Offset transforms are included with the dataset to enable reconstruction of each slab.", - "tags": [ - { - "id": 291, - "tag": "DANDI:000108" - }, - { - "id": 181, - "tag": "DANDI" - } - ], - "timestamp_created": "2023-12-18 13:28:42.711971+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000108/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "167": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230602.1643", - "id": 167, - "name": "Oxytocin neurons enable social transmission of maternal behaviour", - "repository_type": "dandi", - "summary": "Maternal care, including by non-biological parents, is important for offspring survival. Oxytocin which is released by the hypothalamic paraventricular nucleus (PVN), is a critical maternal hormone. In mice, oxytocin enables neuroplasticity in the auditory cortex for maternal recognition of pup distress. However, it is unclear how initial parental experience promotes hypothalamic signalling and cortical plasticity for reliable maternal care. Here we continuously monitored the behaviour of female virgin mice co-housed with an experienced mother and litter. This documentary approach was synchronized with neural recordings from the virgin PVN, including oxytocin neurons. These cells were activated as virgins were enlisted in maternal care by experienced mothers, who shepherded virgins into the nest and demonstrated pup retrieval. Virgins visually observed maternal retrieval, which activated PVN oxytocin neurons and promoted alloparenting. Thus rodents can acquire maternal behaviour by social transmission, providing a mechanism for adapting the brains of adult caregivers to infant needs via endogenous oxytocin.", - "tags": [ - { - "id": 155, - "tag": "oxytocin" - }, - { - "id": 292, - "tag": "alloparenting" - }, - { - "id": 293, - "tag": "maternal behavior" - }, - { - "id": 294, - "tag": "DANDI:000114" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 13:28:48.286680+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000114/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "168": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.220506.1516", - "id": 168, - "name": "Jacobsen 2022", - "repository_type": "dandi", - "summary": "Data for \"All-viral tracing of monosynaptic inputs to single birthdate-defined neurons in the intact brain\", Jacobsen et al 2022. \n\nPhotostimulation laser power: sessions are labelled with the set point power. Actual power can be derived as follows: (set [mW] | actual [mW]), (20 | 7.5), (40 | 15.1)\n", - "tags": [ - { - "id": 299, - "tag": "DANDI:000230" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 13:29:34.268901+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000230/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "169": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.220904.1554", - "id": 169, - "name": "A detailed behavioral, videographic, and neural dataset on object recognition in mice", - "repository_type": "dandi", - "summary": "Mice adeptly use their whiskers to touch, recognize, and learn about objects in their environment. This behavior is enabled by computations performed by populations of neurons in the somatosensory cortex. To understand these computations, we trained mice to use their whiskers to recognize different shapes while we recorded activity in the barrel cortex, which processes whisker input. Here, we present a large dataset of high-speed video of the whiskers, along with rigorous tracking of the entire extent of multiple whiskers and every contact they made on the shape. We used spike sorting to identify individual neurons, which responded with precise timing to whisker contacts and motion. These data will be useful for understanding the behavioral strategies mice use to explore objects, as well as the neuronal dynamics that mediate those strategies. In addition, our carefully curated labeled data could be used to develop new computer vision algorithms for tracking body posture, or for extracting responses of individual neurons from large-scale neural recordings. For further description, see https://www.biorxiv.org/content/10.1101/2022.05.10.491259v1.", - "tags": [ - { - "id": 143, - "tag": "mouse behavior" - }, - { - "id": 144, - "tag": "whisker system" - }, - { - "id": 145, - "tag": "somatosensory cortex" - }, - { - "id": 146, - "tag": "barrel cortex" - }, - { - "id": 147, - "tag": "object recognition" - }, - { - "id": 148, - "tag": "shape discrimination" - }, - { - "id": 35, - "tag": "electrophysiology" - }, - { - "id": 149, - "tag": "pose tracking" - }, - { - "id": 150, - "tag": "population recordings" - }, - { - "id": 151, - "tag": "single unit recordings" - }, - { - "id": 300, - "tag": "DANDI:000231" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 13:29:36.767866+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000231/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "170": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.240510.2038", - "id": 170, - "name": "Rule-based modulation of a sensorimotor transformation across cortical areas", - "repository_type": "dandi", - "summary": "Flexible responses to sensory stimuli based on changing rules are critical for adapting to a dynamic environment. However, it remains unclear how the brain encodes and uses rule information to guide behavior. Here, we made single-unit recordings while head-fixed mice performed a cross-modal sensory selection task where they switched between two rules: licking in response to tactile stimuli while rejecting visual stimuli, or vice versa. Along a cortical sensorimotor processing stream including the primary (S1) and secondary (S2) somatosensory areas, and the medial (MM) and anterolateral (ALM) motor areas, single-neuron activity distinguished between the two rules both prior to and in response to the tactile stimulus. We hypothesized that neural populations in these areas would show rule-dependent preparatory states, which would shape the subsequent sensory processing and behavior. This hypothesis was supported for the motor cortical areas (MM and ALM) by findings that (1) the current task rule could be decoded from pre-stimulus population activity; (2) neural subspaces containing the population activity differed between the two rules; and (3) optogenetic disruption of pre-stimulus states impaired task performance. Our findings indicate that flexible action selection in response to sensory input can occur via configuration of preparatory states in the motor cortex.", - "tags": [ - { - "id": 301, - "tag": "DANDI:000232" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 13:29:38.375239+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000232/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "171": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.250624.0414", - "id": 171, - "name": "A metabolic function of the hippocampal sharp wave-ripple", - "repository_type": "dandi", - "summary": "The hippocampus has previously been implicated in both cognitive and endocrine functions. We simultaneously measured electrophysiological activity from the hippocampus and interstitial glucose concentrations in the body of freely behaving rats to identify an activity pattern that may link these disparate functions of the hippocampus. Here we report that clusters of sharp wave-ripples recorded from the hippocampus reliably predicted a decrease in peripheral glucose concentrations within about 10 min. This correlation was not dependent on circadian, ultradian or meal-triggered fluctuations, could be mimicked with optogenetically induced ripples in the hippocampus (but not in the parietal cortex) and was attenuated to chance levels by pharmacogenetically suppressing activity of the lateral septum, which is the major conduit between the hippocampus and the hypothalamus. Our findings demonstrate that a function of the sharp wave-ripple is to modulate peripheral glucose homeostasis, and offer a mechanism for the link between sleep disruption and blood glucose dysregulation in type 2 diabetes.", - "tags": [ - { - "id": 152, - "tag": "glucose" - }, - { - "id": 302, - "tag": "ecephys" - }, - { - "id": 154, - "tag": "pharmacology" - }, - { - "id": 303, - "tag": "DANDI:000233" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 205, - "tag": "Rattus norvegicus - Norway rat" - } - ], - "timestamp_created": "2023-12-18 13:29:39.790200+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000233/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "172": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230316.1600", - "id": 172, - "name": "Thermoregulatory Responses Forebrain", - "repository_type": "dandi", - "summary": "Imaging in the forebrain of nac -/-; Elavl3-H2B:GCaMP6s +/-; vglut2a-mCherry +/- larval zebrafish during random wave temperature stimulus presentation", - "tags": [ - { - "id": 304, - "tag": "DANDI:000235" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 305, - "tag": "Danio rerio - Zebra fish" - } - ], - "timestamp_created": "2023-12-18 13:29:41.213733+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000235/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "173": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230316.2031", - "id": 173, - "name": "Thermoregulatory Responses Midbrain", - "repository_type": "dandi", - "summary": "Imaging in the midbrain of nac -/-; Elavl3-H2B:GCaMP6s +/-; vglut2a-mCherry +/- larval zebrafish during random wave temperature stimulus presentation", - "tags": [ - { - "id": 306, - "tag": "DANDI:000236" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 305, - "tag": "Danio rerio - Zebra fish" - } - ], - "timestamp_created": "2023-12-18 13:29:42.460396+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000236/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "174": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230316.1655", - "id": 174, - "name": "Thermoregulatory Responses Hindbrain", - "repository_type": "dandi", - "summary": "Imaging in the hindbrain of nac -/-; Elavl3-H2B:GCaMP6s +/-; vglut2a-mCherry +/- larval zebrafish during random wave temperature stimulus presentation", - "tags": [ - { - "id": 307, - "tag": "DANDI:000237" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 305, - "tag": "Danio rerio - Zebra fish" - } - ], - "timestamp_created": "2023-12-18 13:29:43.669483+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000237/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "175": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230316.1519", - "id": 175, - "name": "Thermoregulatory Responses Reticulospinal system", - "repository_type": "dandi", - "summary": "Imaging in the hindbrain and midbrain of nac -/-; Elavl3-H2B:GCaMP6s +/- larval zebrafish after reticulospinal backfills with Texas-Red Dextran during random wave temperature stimulus presentation", - "tags": [ - { - "id": 308, - "tag": "DANDI:000238" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 305, - "tag": "Danio rerio - Zebra fish" - } - ], - "timestamp_created": "2023-12-18 15:07:55.928317+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000238/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "176": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230607.1752", - "id": 176, - "name": "Cortical processing of flexible and context-dependent sensorimotor sequences", - "repository_type": "dandi", - "summary": "The brain generates complex sequences of movements that can be flexibly configured based on behavioural context or real-time sensory feedback, but how this occurs is not fully understood. Here we developed a \u2018sequence licking\u2019 task in which mice directed their tongue to a target that moved through a series of locations. Mice could rapidly branch the sequence online based on tactile feedback. Closed-loop optogenetics and electrophysiology revealed that the tongue and jaw regions of the primary somatosensory (S1TJ) and motor (M1TJ) cortices encoded and controlled tongue kinematics at the level of individual licks. By contrast, the tongue \u2018premotor\u2019 (anterolateral motor) cortex encoded latent variables including intended lick angle, sequence identity and progress towards the reward that marked successful sequence execution. Movement-nonspecific sequence branching signals occurred in the anterolateral motor cortex and M1TJ. Our results reveal a set of key cortical areas for flexible and context-informed sequence generation.", - "tags": [ - { - "id": 309, - "tag": "DANDI:000239" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 15:07:57.339803+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000239/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "177": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 177, - "name": "MRI of human ex vivo brainstem", - "repository_type": "dandi", - "summary": "This dataset contains MRI images associated with an ex vivo specimen of a human brainstem.", - "tags": [ - { - "id": 310, - "tag": "DANDI:000243" - }, - { - "id": 181, - "tag": "DANDI" - } - ], - "timestamp_created": "2023-12-18 15:07:58.650118+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000243/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "178": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 178, - "name": "One photon mesoscale calcium imaging of multiple cell types", - "repository_type": "dandi", - "summary": "One photon dual wavelength mesoscale calcium imaging of mouse isocortex. Includes data from awake and anesthetized subjects with excitatory neuron sensitive GECIs, and data from awake subjects with GECIs sensitive to three different inhibitory interneuron cell types (PV, SOM and VIP).", - "tags": [ - { - "id": 311, - "tag": "DANDI:000244" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 15:07:59.728244+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000244/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "179": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 179, - "name": "developing CaMPARI3", - "repository_type": "dandi", - "summary": "This dataset contains in vivo and in vitro data for the development of CaMPARI3.", - "tags": [ - { - "id": 312, - "tag": "DANDI:000246" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 15:08:01.861963+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000246/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "180": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 180, - "name": "Calcium imaging of egg-laying related neurons in head-fixed Drosophila", - "repository_type": "dandi", - "summary": "Datasets on 2-photon calcium imaging of oviposition descending neurons (oviDNs) as head-fixed flies walk and lay eggs on an agarose-laden wheel. Each NWB file contains time series data from an individual recording session from an individual fly: imaging data, behavior data, and stimulation data (if applicable). A variety of fly genotypes and a variety of environments (egg-laying wheels) were used. Data and methods are described in \"A rise-to-threshold signal for a relative value deliberation\" (https://www.biorxiv.org/content/10.1101/2021.09.23.461548v1). Please contact Vikram Vijayan and/or Gaby Maimon for more information including different download options and different raw/processed data formats.", - "tags": [ - { - "id": 132, - "tag": "Drosophila" - }, - { - "id": 133, - "tag": "egg laying" - }, - { - "id": 134, - "tag": "flies" - }, - { - "id": 20, - "tag": "decision making" - }, - { - "id": 313, - "tag": "rise-to-threshold" - }, - { - "id": 314, - "tag": "DANDI:000247" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 274, - "tag": "Drosophila melanogaster - Fruit fly" - } - ], - "timestamp_created": "2023-12-18 15:08:03.096881+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000247/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "181": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230423.1416", - "id": 181, - "name": "Innate and plastic mechanisms for maternal behaviour in auditory cortex", - "repository_type": "dandi", - "summary": "Infant cries evoke powerful responses in parents. Whether parental animals are intrinsically sensitive to neonatal vocalizations, or instead learn about vocal cues for parenting responses is unclear. In mice, pup-naive virgin females do not recognize the meaning of pup distress calls, but retrieve isolated pups to the nest after having been co-housed with a mother and litte. Distress calls are variable, and require co-caring virgin mice to generalize across calls for reliable retrieval. Here we show that the onset of maternal behaviour in mice results from interactions between intrinsic mechanisms and experience-dependent plasticity in the auditory cortex. In maternal females, calls with inter-syllable intervals (ISIs) from 75 to 375 milliseconds elicited pup retrieval, and cortical responses were generalized across these ISIs. By contrast, naive virgins were neuronally and behaviourally sensitized to the most common (\u2018prototypical\u2019) ISIs. Inhibitory and excitatory neural responses were initially mismatched in the cortex of naive mice, with untuned inhibition and overly narrow excitation. During co-housing experiments, excitatory responses broadened to represent a wider range of ISIs, whereas inhibitory tuning sharpened to form a perceptual boundary. We presented synthetic calls during co-housing and observed that neurobehavioural responses adjusted to match these statistics, a process that required cortical activity and the hypothalamic oxytocin system. Neuroplastic mechanisms therefore build on an intrinsic sensitivity in the mouse auditory cortex, and enable rapid plasticity for reliable parenting behaviour.", - "tags": [ - { - "id": 155, - "tag": "oxytocin" - }, - { - "id": 315, - "tag": "DANDI:000249" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 15:08:04.312017+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000249/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "182": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 182, - "name": "High-resolution tracking of Drosophila during egg-laying", - "repository_type": "dandi", - "summary": "Dataset on the egg-laying behavior of flies used to understand the egg-laying behavioral sequence. Each NWB file contains time series data from an individual fly: the x-y position of the fly, the body length of the fly, egg-deposition moments of the fly, and other behavioral annotations related to egg laying. Data and methods are described in \"A rise-to-threshold signal for a relative value deliberation\" (https://www.biorxiv.org/content/10.1101/2021.09.23.461548v1). Please contact Vikram Vijayan and/or Gaby Maimon for more information including different download options and different raw/processed data formats.", - "tags": [ - { - "id": 132, - "tag": "Drosophila" - }, - { - "id": 133, - "tag": "egg laying" - }, - { - "id": 134, - "tag": "flies" - }, - { - "id": 20, - "tag": "decision making" - }, - { - "id": 316, - "tag": "behavioral sequence" - }, - { - "id": 317, - "tag": "DANDI:000250" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 274, - "tag": "Drosophila melanogaster - Fruit fly" - } - ], - "timestamp_created": "2023-12-18 15:08:05.429412+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000250/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "183": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 183, - "name": "A Unified Framework for Dopamine Signals across Timescales", - "repository_type": "dandi", - "summary": "This dandiset contains the data associated with \"A Unified Framework for Dopamine Signals across Timescales\" (Kim et al. 2020). It is comprised of fiber photometry data, single-unit recordings, stimulus variables, and behavioral measurements across a wide variety of experimental manipulations. ", - "tags": [ - { - "id": 318, - "tag": "DANDI:000251" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 15:08:06.593088+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000251/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "184": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230408.2207", - "id": 184, - "name": "Finger_RL: human intracortical recordings during attempted finger movements of right and left hands", - "repository_type": "dandi", - "summary": "This dataset contains single-neuron recordings from two tetraplegic human participants as they attempted individual finger movements. One participant had an electrode array implanted in the left posterior parietal cortex (PPC) at the junction of the postcentral and intraparietal sulci (PC-IP). The other participant had one electrode array implanted in the hand knob of the left motor cortex (MC) and one electrode array implanted in the superior parietal lobule (SPL) of the left PPC.", - "tags": [ - { - "id": 104, - "tag": "PPC" - }, - { - "id": 7, - "tag": "human" - }, - { - "id": 105, - "tag": "finger" - }, - { - "id": 320, - "tag": "MC" - }, - { - "id": 321, - "tag": "posterior parietal cortex" - }, - { - "id": 28, - "tag": "motor cortex" - }, - { - "id": 322, - "tag": "ipsilateral" - }, - { - "id": 323, - "tag": "DANDI:000252" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 262, - "tag": "Homo sapiens - Human" - } - ], - "timestamp_created": "2023-12-18 16:40:26.690989+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000252/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "185": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 185, - "name": "20220630_AIBS_Patchseq_human", - "repository_type": "dandi", - "summary": "U01 Lein PatchSeq upload June 2022", - "tags": [ - { - "id": 27, - "tag": "Patch-seq" - }, - { - "id": 7, - "tag": "human" - }, - { - "id": 324, - "tag": "DANDI:000288" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 262, - "tag": "Homo sapiens - Human" - } - ], - "timestamp_created": "2023-12-18 16:40:28.289026+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000288/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "186": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.220708.1652", - "id": 186, - "name": "UHN whole-cell patch-clamp excitability recordings from mouse cortical neurons", - "repository_type": "dandi", - "summary": "Whole-cell patch clamp recordings from acute mouse brain slices of layer 5 cortex.", - "tags": [ - { - "id": 6, - "tag": "excitability" - }, - { - "id": 8, - "tag": "cortex" - }, - { - "id": 29, - "tag": "mouse" - }, - { - "id": 325, - "tag": "DANDI:000292" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:40:29.692699+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000292/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "187": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 187, - "name": "A multi-modal fitting approach to construct single-neuron models with patch-clamp and high-density microelectrode arrays", - "repository_type": "dandi", - "summary": "This dataset contains simultaneous intracellular whole-cell patch clamp and extracellular high-density microelectrode array (HD-MEA) recordings.\nIt includes data from two files, corresponding to two recorded neurons. Each NWB file contains several runs of 8 eCode protocols, with syncronized intracellular voltages, stimulus currents, and extracellular voltages.", - "tags": [ - { - "id": 156, - "tag": "HD-MEA, patch-clamp, multimodal" - }, - { - "id": 327, - "tag": "DANDI:000294" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 205, - "tag": "Rattus norvegicus - Norway rat" - } - ], - "timestamp_created": "2023-12-18 16:40:32.060309+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000294/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "188": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 188, - "name": "Electrophysiological properties of adult mouse spinal cord neurons - 22Q2_Ephys_DANDI", - "repository_type": "dandi", - "summary": "Whole-cell patch clamp recording of specific cell types in the adult mouse spinal cord. Neurons are either input defined, output defined or expressing specific molecular markers.", - "tags": [ - { - "id": 328, - "tag": "DANDI:000295" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:40:33.280263+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000295/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "189": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 189, - "name": "UHN whole-cell patch-clamp excitability recordings from human cortical neurons", - "repository_type": "dandi", - "summary": "Whole-cell current clamp recordings from surgically resected human cortical tissue ", - "tags": [ - { - "id": 6, - "tag": "excitability" - }, - { - "id": 7, - "tag": "human" - }, - { - "id": 8, - "tag": "cortex" - }, - { - "id": 330, - "tag": "DANDI:000297" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 262, - "tag": "Homo sapiens - Human" - } - ], - "timestamp_created": "2023-12-18 16:40:35.907148+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000297/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "190": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 190, - "name": "Stephen Test Set", - "repository_type": "dandi", - "summary": "asdfalsdfswadbfnk", - "tags": [ - { - "id": 331, - "tag": "DANDI:000299" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 205, - "tag": "Rattus norvegicus - Norway rat" - } - ], - "timestamp_created": "2023-12-18 16:40:37.084071+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000299/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "191": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230806.0034", - "id": 191, - "name": "Extracellular electrophysiology unit data from mouse Superior Colliculus during whisker guided virtual navigation", - "repository_type": "dandi", - "summary": "Spatial awareness is often intentional, arising from deliberate actions towards a target, while at other times it emerges from unexpected motion in the scene. To navigate the environment, animals must differentiate the spatial cues generated by self-motion from object movements that originate externally. To reveal the neural basis of this ability, we examined the midbrain superior colliculus (SC), which contains multiple egocentric maps of sensorimotor space. By simulating whisker-guided navigation through a dynamic landscape, we discovered a transient neural response that selectively emerged for unexpected, externally generated tactile motion. This transient response only emerged when external motion either gained or lost contact with a whisker, arguing that sensorimotor expectations are specific to a somatotopic location. When external motion sustained contact with the same whiskers, neurons shifted their spike timing to follow the dynamics of self-generated tactile features. Thus, representations based on the timing of self-generated cues may surpass the spatial acuity of the whisker array. In conclusion, the SC contains complementary rate and temporal codes to differentiate external from self-generated tactile features.", - "tags": [ - { - "id": 332, - "tag": "DANDI:000301" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:40:38.322603+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000301/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "192": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 192, - "name": "Habenular neurophysiology", - "repository_type": "dandi", - "summary": "Habenular neurophysiology data associated with Sylwestrak*, Jo*, Vesuna* et al. Cell (2022).", - "tags": [ - { - "id": 333, - "tag": "DANDI:000302" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:40:39.600170+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000302/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "193": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 193, - "name": "20220917_AIBS_Patchseq_human", - "repository_type": "dandi", - "summary": "U01 Lein PatchSeq upload March 2022 - Mansvelder lab data", - "tags": [ - { - "id": 334, - "tag": "DANDI:000337" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 262, - "tag": "Homo sapiens - Human" - } - ], - "timestamp_created": "2023-12-18 16:40:40.883439+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000337/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "194": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 194, - "name": "groupweight BMI", - "repository_type": "dandi", - "summary": "This dataset is the group-weight BMI experiment data. ", - "tags": [ - { - "id": 335, - "tag": "BCI BMI" - }, - { - "id": 336, - "tag": "DANDI:000338" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 210, - "tag": "Macaca mulatta - Rhesus monkey" - } - ], - "timestamp_created": "2023-12-18 16:40:42.177845+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000338/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "195": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 195, - "name": "Local Field Potential Recordings in the Primary Somatosensory Cortex before and after Transcranial Focused Ultrasound Stimulation in Rats", - "repository_type": "dandi", - "summary": "In this study, we investigate how transcranial focused ultrasound (tFUS) modulates neural interaction and response to peripheral electrical stimulation at hindlimb through intracranial multi-electrode recordings in the rat somatosensory cortex. Recordings were performed using a 32-channel NeuroNexus\u00ae multi-electrode array. Rats were anesthetized using isoflurane at 2%. Recordings were taken while delivering peripheral electrical stimulation once every 5 seconds to analyze the neural response in the rat S1HL region, before ultrasound for 30 minutes and then afterwards for an hour, in order to determine how the ultrasound modulated the electrical stimulation induced local field potential (LFP) waveforms. We delivered ultrasound for 5 minutes in a pulsed pattern at 5 levels of sonication repetition frequencies (SRF) (10 Hz, 50 Hz, 75 Hz, 100 Hz, and 125 Hz) to induce frequency dependent plasticity in a manner similar to that is found following tetanic electrical stimulation. We investigated whether delivering tFUS that alters connectivity/correlation between the targeted neurons may alter collective responses manifested in the modulated LFP waveforms. The applied fundamental frequency of ultrasound was 0.5 MHz, the pulse repetition frequency (PRF) was 3 kHz, and the total duty cycle was 36%. This dataset contains LFP recordings, with 32 channels for each datafile.\nIn each subject, recordings are provided with \"pre\" and \"post\" tFUS for two different frequencies (SRF), and most contain recordings during the delivery of ultrasound and during rest periods between sessions of different ultrasound parameters. For example, a single subject may include \"100Hz_pre\", \"100Hz_post\", \"50Hz_pre\", \"50Hz_post\", which are the pre and post tFUS recordings for the two used parameters, and then \"50Hz\" and \"100Hz\", which are the recordings taken during stimulation, and \"rest1\" and \"rest2\", which are the rest periods between sessions. These names are preceded by a label such as \"BH280\", which is the animal subject label. Ultrasound is delivered in a continuous paradigm, with pulses delivered at the stimulation repetition frequency continuously throughout the 5 minutes of stimulation. During the \"pre\" and \"post\" recordings, the peripheral electrical stimulation is delivered once every 5 seconds approximately. Electrical stimulation event trigger is not included in the dataset as it was not recorded due to using a separate system to deliver the electrical stimulation. We were using threshold detection on a channel of LFP recordings in order to detect the electrical stimulation events, in which events can be recognized by the quick rising of voltage. One could also approximate it by detecting the first event, and then add periods of 5 seconds to generate the rest of the trigger times.\nMore details about the experimental details can be found in our paper published by Journal of Neural Engineering (DOI: 10.1088/1741-2552/ac889f).", - "tags": [ - { - "id": 119, - "tag": "Ultrasound" - }, - { - "id": 122, - "tag": "tFUS" - }, - { - "id": 120, - "tag": "Plasticity" - }, - { - "id": 337, - "tag": "Somatosensory" - }, - { - "id": 121, - "tag": "Rat" - }, - { - "id": 123, - "tag": "Somatosensory Cortex" - }, - { - "id": 338, - "tag": "DANDI:000339" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 205, - "tag": "Rattus norvegicus - Norway rat" - } - ], - "timestamp_created": "2023-12-18 16:40:43.497224+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000339/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "196": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.241220.1312", - "id": 196, - "name": "Temporal disparity of action potentials triggered in axon initial segments and distal axons in the neocortex", - "repository_type": "dandi", - "summary": "Patch-clamp recordings of Layer 1 interneurons in the human and rodent neocortex. Data comes from Gabor Tamas Lab (ELKH-SZTE Research Group for Cortical Microcircuits; University of Szeged, Hungary).", - "tags": [ - { - "id": 339, - "tag": "Layer 1 interneurons" - }, - { - "id": 7, - "tag": "human" - }, - { - "id": 340, - "tag": "rodent" - }, - { - "id": 341, - "tag": "in vitro" - }, - { - "id": 342, - "tag": "in vivo" - }, - { - "id": 343, - "tag": "Retroaxonal firing" - }, - { - "id": 344, - "tag": "Persistent firing" - }, - { - "id": 345, - "tag": "Retoaxonal action potentials" - }, - { - "id": 346, - "tag": "DANDI:000341" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 205, - "tag": "Rattus norvegicus - Norway rat" - } - ], - "timestamp_created": "2023-12-18 16:40:44.762552+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000341/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "197": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.221208.1803", - "id": 197, - "name": "Scalable Thousand Channel Penetrating Microneedle Arrays on Flex for Multimodal and Large Area Coverage BrainMachine Interfaces", - "repository_type": "dandi", - "summary": "The Utah array powers cutting-edge projects for restoration of neurological function, such as BrainGate, but the underlying electrode technology has itself advanced little in the last three decades. Here, advanced dual-side lithographic microfabrication processes is exploited to demonstrate a 1024-channel penetrating silicon microneedle array (SiMNA) that is scalable in its recording capabilities and cortical coverage and is suitable for clinical translation. The SiMNA is the first penetrating microneedle array with a flexible backing that affords compliancy to brain movements. In addition, the SiMNA is optically transparent permitting simultaneous optical and electrophysiological interrogation of neuronal activity. The SiMNA is used to demonstrate reliable recordings of spontaneous and evoked field potentials and of single unit activity in chronically implanted mice for up to 196 days in response to optogenetic and to whisker air-puff stimuli. Significantly, the 1024-channel SiMNA establishes detailed spatiotemporal mapping of broadband brain activity in rats. This novel scalable and biocompatible SiMNA with its multimodal capability and sensitivity to broadband brain activity will accelerate the progress in fundamental neurophysiological investigations and establishes a new milestone for penetrating and large area coverage microelectrode arrays for brain\u2013machine interfaces.", - "tags": [ - { - "id": 351, - "tag": "DANDI:000398" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:40:52.742527+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000398/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "198": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 198, - "name": "All-optical physiology resolves a synaptic basis for behavioral time scale plasticity", - "repository_type": "dandi", - "summary": "Data included in Fan (2022) Cell", - "tags": [ - { - "id": 352, - "tag": "DANDI:000399" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:40:53.885144+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000399/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "199": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230307.2132", - "id": 199, - "name": "MICrONS Two Photon Functional Imaging", - "repository_type": "dandi", - "summary": "The light microscopic images were acquired from a cubic millimeter volume that spanned portions of primary visual cortex and three higher visual cortical areas. The volume was imaged in vivo by two-photon random access mesoscope (2P-RAM) from postnatal days P75 to P81 in a male mouse expressing a genetically encoded calcium indicator in excitatory cells, while the mouse viewed natural movies and parametric stimuli. The calcium imaging data includes the single-cell responses of an estimated 75,000 pyramidal cells imaged over a volume of approximately 1200 x 1100 x 500 \u03bcm3 (anteroposterior x mediolateral x radial depth). The center of the volume was placed at the junction of primary visual cortex (VISp) and three higher visual areas, lateromedial area (VISlm), rostrolateral area (VISrl) and anterolateral area (VISal). During imaging, the animal was head-restrained, and the stimulus was presented to the left visual field. Treadmill rotation (single axis) and video of the animal's left eye were captured throughout the scan, yielding the locomotion velocity, eye movements, and pupil diameter data included here.\n\nThe functional data were co-registered with electron microscopy (EM) data. The structural identifiers of the matched cells are added as plane segmentation columns extracted from the CAVE database. To access the latest revision see the notebook that is linked to this dandiset. The structural ids might not be present for all plane segmentations.", - "tags": [ - { - "id": 353, - "tag": "DANDI:000402" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:40:55.290483+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000402/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "200": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230605.2024", - "id": 200, - "name": "Monkey 2D cursor BMI", - "repository_type": "dandi", - "summary": "This dataset includes binned spike count data (chronic microwire arrays in PMd/M1) and brain-machine-interface behavioral data (2D cursor kinematics, target locations, trials) from Athalye, V*, Khanna, P*, Gowda S, Orsborn, AL, Costa RM**, Carmena, JC**, (2023) \"Invariant neural dynamics drive commands to control different movements\": https://www.biorxiv.org/content/10.1101/2021.08.27.457931v2. \n\nFor more information about this data, please contact Vivek Athalye and/or Preeya Khanna. \n\nCode for analyzing this data and re-creating manuscript figures is located: https://github.com/pkhanna104/bmi_dynamics_code and archived at https://zenodo.org/record/8006653", - "tags": [ - { - "id": 354, - "tag": "neural population dynamics" - }, - { - "id": 28, - "tag": "motor cortex" - }, - { - "id": 355, - "tag": "motor control" - }, - { - "id": 356, - "tag": "brain-machine interface" - }, - { - "id": 357, - "tag": "neuroprosthetics" - }, - { - "id": 358, - "tag": "optimal feedback control" - }, - { - "id": 359, - "tag": "motor commands" - }, - { - "id": 360, - "tag": "movement representations" - }, - { - "id": 361, - "tag": "dynamical systems" - }, - { - "id": 362, - "tag": "DANDI:000404" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 210, - "tag": "Macaca mulatta - Rhesus monkey" - } - ], - "timestamp_created": "2023-12-18 16:40:56.859123+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000404/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "201": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 201, - "name": "Gonzalez & Giocomo (2022) Parahippocampal neurons encode task-relevant information for goal-directed navigation", - "repository_type": "dandi", - "summary": "Data used in the Gonzalez & Giocomo (2022) manuscript. This contains session electrophysiological data and accompanying event metadata/time-series necessary to generate figures in the manuscript. Github link contains metadata files that contain information for each session, including if it is an open-field foraging session or a Tree-Maze session, and how many units were collected in it. Additional metadata includes unit matching across session and a detailed behavioral performance table.\n\nPre-print DOI: \nhttps://doi.org/10.1101/2022.12.15.520660\n\nGithub:\nhttps://github.com/alexgonzl/TMA\n", - "tags": [ - { - "id": 363, - "tag": "DANDI:000405" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 205, - "tag": "Rattus norvegicus - Norway rat" - } - ], - "timestamp_created": "2023-12-18 16:40:58.009663+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000405/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "202": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 202, - "name": "IBL - Brain Wide Map [deprecated]", - "repository_type": "dandi", - "summary": "The International Brain lab (IBL) aims to understand the neural basis of decision-making in the mouse by gathering a whole-brain activity map composed of electrophysiological recordings pooled from multiple laboratories. We have systematically recorded from nearly all major brain areas with Neuropixels probes, using a grid system for unbiased sampling and replicating each recording site in at least two laboratories. These data have been used to construct a brain-wide map of activity at single-spike cellular resolution during a decision-making task. In addition to the map, this data set contains other information gathered during the task: sensory stimuli presented to the mouse; mouse decisions and response times; and mouse pose information from video recordings and DeepLabCut analysis.", - "tags": [ - { - "id": 80, - "tag": "International Brain Laboratory" - }, - { - "id": 364, - "tag": "multi-probe" - }, - { - "id": 365, - "tag": "Neuropixels" - }, - { - "id": 366, - "tag": "Allen Mouse Brain CCFv3" - }, - { - "id": 367, - "tag": "Allen Mouse Brain Atlas" - }, - { - "id": 368, - "tag": "head-fixed" - }, - { - "id": 84, - "tag": "decision-making" - }, - { - "id": 369, - "tag": "face-tracking" - }, - { - "id": 370, - "tag": "DANDI:000409" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:40:59.347570+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000409/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "203": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 203, - "name": "Joshi et al (2023) Dynamic Synchronization between Hippocampal Spatial Representations and the Stepping Rhythm", - "repository_type": "dandi", - "summary": "This dateset includes electrophysiological data from from dorsal CA1 of rats running on a linear or w-shaped track. Please contact Abhilasha Joshi or Loren Frank for more information about this data. ", - "tags": [ - { - "id": 371, - "tag": "DANDI:000410" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 205, - "tag": "Rattus norvegicus - Norway rat" - } - ], - "timestamp_created": "2023-12-18 16:41:00.569329+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000410/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "204": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 204, - "name": "test", - "repository_type": "dandi", - "summary": "It is a test to use DANDI archive, like hello world. I will not upload any neural data in this set.", - "tags": [ - { - "id": 372, - "tag": "DANDI:000411" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:41:01.735553+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000411/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "205": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.250724.2224", - "id": 205, - "name": "Novel-familiar-novel WTrack (CA1-PFC)", - "repository_type": "dandi", - "summary": "Hippocampal-prefrontal recordings in rats (dorsal CA1 region of the hippocampus and primarily the prelimbic region of prefrontal cortex) during exposure to novel and familiar WTrack environments. Data includes spikes from single units, local field potential recordings, position (x, y, velocity), and trajectory information. Each file contains data from 3 behavioral epochs - Novel Wtrack (first exposure) - Familiar WTrack - Novel WTrack (second exposure)", - "tags": [ - { - "id": 373, - "tag": "Hippocampus, Prefrontal cortex, Learning, Memory, Decision making" - }, - { - "id": 374, - "tag": "DANDI:000447" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 205, - "tag": "Rattus norvegicus - Norway rat" - } - ], - "timestamp_created": "2023-12-18 16:41:02.889249+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000447/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "206": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230421.1844", - "id": 206, - "name": "Time kinetics of the membrane potential at the cathode- and anode-facing poles of a cell induced by a train of 5 pulses at 833 kHz", - "repository_type": "dandi", - "summary": "Time-lapse recordings for the investigation of the relationship between the electroporation efficiency of nsEP and changes in the transmembrane potential (TMP). A strobe imaging synchronized with nsEP exposure was used. This allowed studying the nanosecond kinetics of TMP charging and relaxation, which is the primary effect of nsEP that rapidly charges the plasma membrane. The methodology involved pulsed laser fluorescence microscopy with voltage-sensitive FluoVolt dye-loaded cells that respond to TMP changes within nanoseconds. FluoVolt has a high sensitivity of about 10 % deltaF/F per 100 mV. During the imaging, a single short pulse laser flash is delivered at a precise time interval (addition of 25ns in each frame) before, during, or after nsEP exposure. The camera shutter opens in advance of and closes after the laser flash, capturing one TMP image per nsEP exposure. Supported by NIH 1R21EY034258", - "tags": [ - { - "id": 375, - "tag": "DANDI:000448" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 376, - "tag": "Cricetulus griseus - Cricetulus aureus" - } - ], - "timestamp_created": "2023-12-18 16:41:04.313793+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000448/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "207": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230302.2331", - "id": 207, - "name": "Guide to the construction and use of an adaptive optics two-photon microscope with direct wavefront sensing", - "repository_type": "dandi", - "summary": "Two-photon microscopy, combined with appropriate optical labeling, has enabled the study of structure and function throughout animals and their organ systems, especially nervous systems. This methodology enables, for example, the measurement and tracking of sub-micrometer structures within brain cells, the spatio-temporal mapping of spikes in individual neurons, and the spatio-temporal mapping of transmitter release in individual synapses. Yet the spatial resolution of two-photon microscopy rapidly degrades as imaging is attempted at depths more than a few scattering lengths into tissue, i.e., below the superficial layers that constitute the top 300 to 400\u00a0\u00b5m of neocortex. To obviate this limitation, we measure the wavefront of the guide star at the focus of the excitation beam and utilize adaptive optics that alters the incident wavefront to achieve an improved focal volume. We describe the construction, calibration, and operation of a two-photon microscope that incorporates adaptive optics to restore diffraction-limited resolution throughout the nearly 900\u00a0\u00b5m depth of mouse cortex. Our realization utilizes a guide star formed by excitation of red-shifted dye within the blood serum to directly measure the wavefront. We incorporate predominantly commercial optical, optomechanical, mechanical, and electronic components; computer aided design models of the exceptional custom components are supplied. The resultant adaptive-optics two-photon microscope is modular and allows for expanded imaging and optical excitation capabilities. We demonstrate our methodology in mouse neocortex by imaging the morphology of somatostatin-expressing neurons that lie 700 \u00b5m beneath the pia, calcium dynamics of layer 5b projection neurons, and thalamocortical glutamate transmission to L4 neurons.", - "tags": [ - { - "id": 377, - "tag": "DANDI:000454" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:41:05.507459+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000454/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "208": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230317.0039", - "id": 208, - "name": "Simultaneous electroencephalography, extracellular electrophysiology, and cortical electrical stimulation in head-fixed mice", - "repository_type": "dandi", - "summary": "In this set of experiments, we recorded neural signals using an electroencephalography (EEG) array and Neuropixels probes while directly electrically stimulating the cortex in awake and anesthetized head-fixed mice. Details of the experiments can be found in the associated manuscript (Claar, Rembado et al., 2023). Briefly, the recordings and stimulation commenced while the mouse was awake, free to rest or move on a rotating disc. After delivering a series of stimuli, anesthesia was induced with isoflurane (5% via inhalation). Once a surgical level of anesthesia was reached, the mouse was maintained unconscious (1-1.5% isoflurane) during the delivery of another series of stimuli. In some subjects another series of stimuli was delivered after the isoflurane was turned off, this epoch/state is referred to as \u201crecovery\u201d in the dataset.\n\nEach session file includes the following data: raw EEG signals from all 30 surface electrodes and the associated electrode information; raw LFP signals from all electrodes on all Neuropixels probes (if used) and the associated electrode information; spike times for all units (spike sorted with Kilosort 2.0) that passed our quality threshold with associated information (brain region, spike waveform duration, etc.); subject\u2019s speed computed from the rotation of the disc for the entire session; epochs marking when isoflurane was induced and when it was maintained; and a trial table containing information about every stimulus delivered throughout the session.\n\nAll sessions included an awake and an isoflurane epoch, but not all sessions included a recovery epoch. Some subjects received visual stimulation trials (visual stimuli presented on a screen in the subject\u2019s right field of view) interleaved with the electrical stimulation trials.", - "tags": [ - { - "id": 378, - "tag": "EEG" - }, - { - "id": 365, - "tag": "Neuropixels" - }, - { - "id": 379, - "tag": "electrical stimulation" - }, - { - "id": 380, - "tag": "brain states" - }, - { - "id": 381, - "tag": "cortico-thalamic interactions" - }, - { - "id": 52, - "tag": "extracellular electrophysiology" - }, - { - "id": 382, - "tag": "DANDI:000458" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:41:06.667095+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000458/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "209": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 209, - "name": "Cohen Tickertapes Exploratory Data 1", - "repository_type": "dandi", - "summary": "Exploratory dataset for the second generation of molecular tickertapes in the Adam Cohen Lab.", - "tags": [ - { - "id": 383, - "tag": "DANDI:000461" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 384, - "tag": "Canis lupus familiaris - Dog" - } - ], - "timestamp_created": "2023-12-18 16:41:07.984083+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000461/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "210": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230416.2132", - "id": 210, - "name": "HippocampusRewardDataset", - "repository_type": "dandi", - "summary": "Dataset that applies to the paper.\nKrishnan, S., Heer, C., Cherian, C. et al. Nat Commun 13, 6662 (2022). https://doi.org/10.1038/s41467-022-34465-5\n\nUploaded datasets include data from WT mice where mice underwent a reward expectation extinction task as described in Figures 1-3. Four datasets with DREADD manipulation of VTA dopaminergic neurons and controls are also included as described in Figure 4. All datasets are processed raw fluorescence obtained from suite2p. Behavior data is also included.\n\nScripts used for analysis can be found on https://github.com/seethakris/HPCrewardpaper", - "tags": [ - { - "id": 385, - "tag": "hippocampus; dopamine; mice; reward; calcium imaging; VR based navigation; DREADD" - }, - { - "id": 386, - "tag": "DANDI:000462" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:41:09.224047+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000462/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "211": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.250711.1947", - "id": 211, - "name": "Electrophysiological Recordings in Anesthetized Rats in the Primary Somatosensory Cortex with Phased Ultrasound Array Stimulation", - "repository_type": "dandi", - "summary": "In these recordings, we test different intensities and PRFs (pulse repetition frequencies) of ultrasound stimulation using a phased ultrasound array to stimulate the somatosensory cortex of rats anesthetized with 2% isoflurane through inhalation. Recordings are taken using 32 channel Neuronexus electrodes. Ultrasound stimulation is delivered every 2.5 seconds, and each recording has either 200 or 500 trials. Detailed description about this dataset can be found in the following publication. Please cite the paper if you would use a portion of the dataset. Gao, H., Ramachandran, S., Yu, K., & He, B. (2025). Transcranial Focused Ultrasound Modulates Feedforward and Feedback Cortico-Thalamo-Cortical Pathways by Selectively Activating Excitatory Neurons. The Journal of Neuroscience, 45(23), e2218242025. https://doi.org/10.1523/JNEUROSCI.2218-24.2025", - "tags": [ - { - "id": 387, - "tag": "DANDI:000463" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 205, - "tag": "Rattus norvegicus - Norway rat" - } - ], - "timestamp_created": "2023-12-18 16:41:10.304293+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000463/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "212": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230530.2349", - "id": 212, - "name": "Human brain mapping with multithousand-channel PtNRGrids resolves spatiotemporal dynamics", - "repository_type": "dandi", - "summary": "This dataset contains recordings from PtNRGrids, which are devices designed to record electrocorticography (ECoG) activities from the brain surface at high-spatial resolution. PtNRGrids used here were a square-shaped array with 1024 channels and a 150-\u00b5m pitch, which was implanted to record neural activity from the entire right primary somatosensory barrel cortex. The sensory response on the brain was evoked by delivering air puffs through a microcapillary tube to individually stimulate individual whiskers on the contralateral side. This dataset demonstrates the high-spatial resolution recording capability of PtNRGrids isolate functional cortical columns in sub-mm resolution from the surface of the brain.\n\n[Publication corresponding to this dataset] Tchoe, Youngbin, et al. \"Human brain mapping with multithousand-channel PtNRGrids resolves spatiotemporal dynamics.\" Science translational medicine 14.628 (2022): eabj1441.\n[Electrode mapping information & Basic analysis codes] Github: https://ytchoe.github.io/", - "tags": [ - { - "id": 388, - "tag": "micro-ECoG, barrel cortex, high gamma activity" - }, - { - "id": 389, - "tag": "DANDI:000465" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 205, - "tag": "Rattus norvegicus - Norway rat" - } - ], - "timestamp_created": "2023-12-18 16:41:11.470240+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000465/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "213": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 213, - "name": "Sparse and stereotyped encoding implicates a core glomerulus for ant alarm behavior", - "repository_type": "dandi", - "summary": "In these experiments, we recorded odor-evoked calcium responses in the antennal lobe of the clonal raider ant (Ooceraea biroi). Transgenic ants expressed GCaMP6s in olfactory sensory neurons, and the right antennal lobe was imaged using a two-photon microscope. A piezo device and resonant scanning galvanometer were used to image whole-antennal lobe volumes at a rate of 0.83 volumes per second. 33 z-planes were imaged at 5 micrometer intervals. In each recording, odor stimulus commenced after 3s and lasted for 5s. Negative control: paraffin oil. General odorant stimuli: isopropanol, ethylpyrazine, ethanol, 3-hexanone, propionic acid. Ant alarm pheromones: 4-methyl-3-heptanone, 4-methyl-3-heptanol, 4-methyl-3-hexanol, 6-methyl-5-hepten-2-one.\n\nExperiment key:\nGeneral odorant experiment subjects:\nsub-ant1-m10-d6-y2022\nsub-ant3-m10-d6-y2022\nsub-ant1-m10-d11-y2022\nsub-ant2-m10-d11-y2022\nsub-ant3-m10-d11-y2022\nsub-ant5-m10-d11-y2022\n\nBilateral antennal lobe imaging experiment subjects:\nsub-ant2-m2-d10-y2023\nsub-ant3-m2-d10-y2023\nsub-ant4-m2-d10-y2023\n\nUnilateral alarm pheromone imaging experiment subjects:\nsub-ant2-m4-d20-y2022\nsub-ant4-m4-d20-y2022\nsub-ant1-m4-d21-y2022\nsub-ant2-m4-d21-y2022\nsub-ant3-m4-d21-y2022\nsub-ant1-m4-d26-y2022\nsub-ant2-m4-d26-y2022\nsub-ant3-m4-d26-y2022\nsub-ant4-m4-d26-y2022\nsub-ant5-m4-d26-y2022\nsub-ant1-m4-d27-y2022\nsub-ant2-m4-d27-y2022\nsub-ant3-m4-d27-y2022", - "tags": [ - { - "id": 390, - "tag": "antennal lobe; calcium imaging; chemosensation; clonal raider ant; communication; GCaMP; odor coding; olfaction; Ooceraea biroi; pheromone" - }, - { - "id": 391, - "tag": "DANDI:000467" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 392, - "tag": "Ooceraea biroi - Clonal raider ant" - } - ], - "timestamp_created": "2023-12-18 16:41:12.816150+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000467/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "214": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.241009.1502", - "id": 214, - "name": "NeuroPAL volumetric images", - "repository_type": "dandi", - "summary": "Partially labeled NeuroPAL volumetric images produced by the Kato lab at UCSF as part of an effort to add to the corpus of existing labeled data to facilitate the development of automatic cell identification algorithms. We hope that making these datasets more easily accessible in a centralized location like DANDI will significantly speed up development by allowing new researchers to start working on algorithms without the need to build up their own full dataset of training data beforehand. \n\nAs part of this effort, we also propose a novel extension to the NWB data format to allow for multichannel volumetric data.\n\nContents:\n\nSubject (C. elegans subject) - contains relevant metadata about the experimental subject\n\nAcquisition, \"NeuroPALImageRaw\" (MultiChannelVolume) - contains the raw image and associated metadata with dimensions (X, Y, Z, C)\n\nProcessing, \"NeuroPAL\", \"VolumeSegmentation\" (VolumeSegmentation) - contains a list of neuron centers and associated IDs (X, Y, Z, weight, ID)\n\nProcessing, \"NeuroPAL\", \"ImagingVolume\" (ImagingVolume) - contains the metadata associated with the imaging acquisition\n\nProcessing, \"NeuroPAL\", \"OpticalChannelRefs\" (OpticalChannelReferences) - contains the order of the optical channels in the image\n\nProcessing, \"ProcessedImage\", \"Hist_match_med_filt\" - contains the processed NeuroPAL image with dimensions (X,Y,Z,C)", - "tags": [ - { - "id": 393, - "tag": "DANDI:000472" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 394, - "tag": "Caenorhabditis elegans" - } - ], - "timestamp_created": "2023-12-18 16:51:25.267811+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000472/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "215": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230417.1502", - "id": 215, - "name": "Esr1+ hypothalamic-habenula neurons shape aversive states.", - "repository_type": "dandi", - "summary": "Prefrontal cortex (PFC) high-density extracellular recordings (Neuropixels) in head-fixed mice during an aversive-conditioning task. Neuronal responses to internally generated (i.e. pathway-specific optogenetic activation, see the main article for details) and externally derived (i.e. air puffs) aversive signals have been recorded.", - "tags": [ - { - "id": 365, - "tag": "Neuropixels" - }, - { - "id": 79, - "tag": "Mouse" - }, - { - "id": 395, - "tag": "Head-fixed" - }, - { - "id": 396, - "tag": "Lateral Hypothalamus" - }, - { - "id": 397, - "tag": "Lateral Habenula" - }, - { - "id": 398, - "tag": "Prefrontal cortex" - }, - { - "id": 399, - "tag": "Aversion" - }, - { - "id": 400, - "tag": "DANDI:000473" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:51:26.503022+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000473/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "216": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230417.2148", - "id": 216, - "name": "State-dependent processing in visual cortex", - "repository_type": "dandi", - "summary": "Extracellular recordings from rabbit visual cortex neurons. Orientation testing with drifting grating stimuli. Hippocampal EEG for the state scoring procedure. ", - "tags": [ - { - "id": 401, - "tag": "electrophysiology, signal processing" - }, - { - "id": 402, - "tag": "DANDI:000481" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 403, - "tag": "Oryctolagus cuniculus - Rabbits" - } - ], - "timestamp_created": "2023-12-18 16:51:28.041587+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000481/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "217": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 217, - "name": "State-dependent processing in visual cortex", - "repository_type": "dandi", - "summary": "Test upload. Extracellular recordings from rabbit visual cortex neurons. Orientation testing with drifting grating stimuli. Hippocampal EEG for the state scoring procedure.", - "tags": [ - { - "id": 404, - "tag": "DANDI:000482" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 403, - "tag": "Oryctolagus cuniculus - Rabbits" - } - ], - "timestamp_created": "2023-12-18 16:51:29.171313+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000482/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "218": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230421.2321", - "id": 218, - "name": "Dataset for \"Coregistration of heading to visual cues in retrosplenial cortex\"", - "repository_type": "dandi", - "summary": "These are all the accompanying data for the following publication:\nSit, K.K., Goard, M.J. Coregistration of heading to visual cues in retrosplenial cortex. Nat Commun 14, 1992 (2023). https://doi.org/10.1038/s41467-023-37704-5", - "tags": [ - { - "id": 405, - "tag": "DANDI:000483" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:51:30.385064+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000483/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "219": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230602.2022", - "id": 219, - "name": "Allen Institute Openscope - Differential encoding of temporal context and expectation", - "repository_type": "dandi", - "summary": "This dataset was collected for the Predictive Coding project, as part of the Allen Institute for Brain Science's OpenScope project. \n\nThe experimental design involved visual stimulation with sequences of 4 natural scene images (ABCD) that are repeated many times, with an occasional rare oddball image in the 4th place in the sequence (ABCX). There are 10 unique oddball images which are each shown 10 times during the recording session. The main sequence (ABCD) is shown thousands of times. In addition to the oddball blocks there are control conditions where the 4 main sequence images and 10 oddball images are shown either entirely randomly, or where pairwise image transitions are maintained, but the overall sequence is shuffled (example pairs: AB, BC, CD, CX, XA, etc). \n\nDuring this stimulus paradigm, 2-photon calcium imaging was used to record neural activity in three cortical areas (one area per recording session): the primary visual cortex, higher order visual area PM, and the retrosplenial cortex, across multiple cortical depths. During the recordings, mice were free to run on a circular disk and running speed was measured, along with pupil diameter and eye position. In each mouse, two of the three areas were imaged, and the third had a retrograde tracer (rAAV-mRuby2) injected to label inputs to that region. No differences in physiology were identified between retrogradely labeled and non-labeled neurons, thus this information was not included in the primary study of this dataset. \n\nResults are provided in the form of normalized calcium traces (dF/F) for all recorded neurons, along with running speed, pupil measurements, and the timing of all stimulus events. ", - "tags": [ - { - "id": 40, - "tag": "neocortex" - }, - { - "id": 58, - "tag": "pyramidal neurons" - }, - { - "id": 63, - "tag": "two-photon calcium imaging" - }, - { - "id": 64, - "tag": "mouse VisP" - }, - { - "id": 65, - "tag": "prediction" - }, - { - "id": 406, - "tag": "predictive coding" - }, - { - "id": 407, - "tag": "DANDI:000488" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:51:31.724978+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000488/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "220": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230518.1811", - "id": 220, - "name": "The impact of the second phase amplitude (% to the first phase) on the electroporation efficiency (measured as YP emission)", - "repository_type": "dandi", - "summary": "Cells were electroporated by a train of 5 unipolar 600 ns pulses (0 percent) or bipolar 600 + 600 ns pulses (25, 50, 75, 100 percent) at 1 Hz. 25, 50, 75, and 100% represent the second pulse's amplitude in relation to the first pulse's amplitude. 0% represents a unipolar pulse, where no second pulse was applied. The electrodes were two parallel stainless-steel cylinders, 1.5 mm in diameter and 3 mm apart. Supported by NIH 1R21EY034258", - "tags": [ - { - "id": 408, - "tag": "DANDI:000489" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 409, - "tag": "Bos taurus - Cattle" - } - ], - "timestamp_created": "2023-12-18 16:51:33.153996+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000489/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "221": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230602.1307", - "id": 221, - "name": "BrainFlowZZZ", - "repository_type": "dandi", - "summary": "Dataset from the 2023 manuscript titled **_Sizes and Shapes of Perivascular Spaces Surrounding Murine Pial Arteries_** by Raicevic et al. DOI: 10.21203/rs.3.rs-2587250/v1. \n\n## Overview\nThe **14 datasets** from **9 subjects** include the original 3D two photon microscopy data from three channels which show tracer in the vessel, PVSs, and microspheres. Additionally, each dataset also includes the final binary segmentation of the PVS and vessel used to generate the model and statistics in the manuscript. Additional details regarding the subjects, tracer injection, image acquisition, and segmentation can be found in the manuscript at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9949243/.\n\n## Content\nFor easier navigation, below is a mapping between the NWB file names and the datasets referenced in the manuscript.\n1. sub-21-07-19-b-act (Mouse 6, dataset K)\n2. sub-21-09-01-b-act (Mouse 8, dataset M)\n3. sub-21-09-20-b-act (Mouse 9, dataset N)\n4. sub-21-10-08-b-act (Mouse 7, dataset L)\n5. sub-BPN-M4 (Mouse 3, dataset E and F)\n6. sub-BPN-M6 (Mouse 4, dataset G and H)\n7. sub-BPN-M7 (Mouse 5, dataset I and J)\n8. sub-BPN-OLD-M2 (Mouse 1, dataset A and B)\n9. sub-BPN-OLD-M3 (Mouse 2, dataset C and D)\n\n## Data Reading Instructions\nThe .nwb files can be viewed using PyNWB or MatNWB. To install and set up, please visit . Below, we show how to open and view an .nwb file using MatNWB. \n### Loading the image data\n```matlab\nnwb = nwbRead(PATH_TO_NWB_FILE);\n\n% first check what color channels are present\n>> nwb.acquisition\nans = \n 3\u00d71 Set array with properties:\n TwoPhotonSeriesChanA: [types.core.TwoPhotonSeries]\n TwoPhotonSeriesChanB: [types.core.TwoPhotonSeries]\n TwoPhotonSeriesChanC: [types.core.TwoPhotonSeries]\n```\nThe above code load the .nwb file and the output tells us that there are three channels present in the nwb file, which are ChanA, ChanB, and ChanC. Then, to load the actual data from a channel,\n```matlab\n% load the image data from ChanA\n>> chanAdata = nwb.acquisition.get('TwoPhotonSeriesChanA').data.load();\n\n% check its shape\n>> size(chanAdata)\nans =\n 1 512 512 181\n```\n### Loading the segmentation masks\nFor an overview of the mask for ChanA, for example,\n```matlab\n>> nwb.processing.get('ophys').nwbdatainterface.get('ImageSegmentation').planesegmentation.get('PlaneSegmentationChanA').image_mask.data\nans = \n DataStub with properties:\n filename: '.\\sub-BPN-M4_ses-20210524-m1_obj-1c8nyxo_ophys.nwb'\n path: '/processing/ophys/ImageSegmentation/PlaneSegmentationChanA/image_mask'\n dims: [512 512 181]\n ndims: 3\n dataType: 'logical'\n```\nTo load the actual mask data into array (may take several seconds to load),\n```matlab\n% load mask from ChanA\n>> mask = nwb.processing.get('ophys').nwbdatainterface.get('ImageSegmentation').planesegmentation.get('PlaneSegmentationChanA').image_mask.data.load();\n\n% check its shape\n>> size(mask)\nans =\n 512 512 181\n```", - "tags": [ - { - "id": 410, - "tag": "DANDI:000491" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:51:34.468218+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000491/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "222": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 222, - "name": "Test 2", - "repository_type": "dandi", - "summary": "try to upload EIS", - "tags": [ - { - "id": 411, - "tag": "DANDI:000529" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 205, - "tag": "Rattus norvegicus - Norway rat" - } - ], - "timestamp_created": "2023-12-18 16:51:35.586711+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000529/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "223": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230524.0416", - "id": 223, - "name": "Allen Institute Openscope - Effects of Periodic Visual Stimulation on Neural Activity in Mouse Visual Cortex", - "repository_type": "dandi", - "summary": "This study investigates the effects of periodic sensory stimulation (PSS) on the activity of neurons of different types in the mouse visual cortex. The PSS modality used is the flickering light visual stimulation. The dataset provides calcium imaging data from VIP (vasoactive intestinal peptide), SST (somatostatin), and PV (parvalbumin) expressing inhibitory neuron, as well as excitatory neurons in V1, in mice exposed to different visual stimulation conditions.\n \nThe visual stimulus is provided by an LED strip that is programmed for 4 conditions: 40 Hz flicker (gamma frequency), 8 Hz flicker (theta frequency), random frequency (light delivered with a random interval determined by a Poisson process with an average frequency of 40 Hz), and constant light on. Those conditions are reported in NWB file trial table. The LED strip was positioned in front of the mouse right eye in place of the visual stimulation screen used in (De Vries, Lecoq, Buice et al, Nature Neuroscience 2020). Light illumination started approximately 485 seconds into the experiment. The exact timing can be found in the stimulus table. Each mouse was recorded multiple times with individual cells matched. Each recording session only used one of the above stimulus conditions.\n \nThe data includes images from layers 2/3 and 5 (layer 4 instead of 5 for VIP) of the left visual cortex, capturing the contributions of superficial and deep cortical neurons to the network dynamics under visual PSS. The dataset was collected from 5 mice for SST, 6 mice for VIP, 2 mice for PV, and 2 mice for excitatory neurons. Mice were awake but not trained to perform any tasks. They were passively viewing the LED strip. All animal procedures were approved by the Institutional Animal Care and Use Committee (IACUC) at the Allen Institute for Brain Science in compliance with NIH guidelines.", - "tags": [ - { - "id": 412, - "tag": "two-photon" - }, - { - "id": 413, - "tag": "cortical recording" - }, - { - "id": 414, - "tag": "gamma" - }, - { - "id": 415, - "tag": "neuroprotection" - }, - { - "id": 14, - "tag": "oscillations" - }, - { - "id": 54, - "tag": "visual stimuli" - }, - { - "id": 416, - "tag": "DANDI:000535" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:51:36.817084+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000535/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "224": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 224, - "name": "Scaling of GEVI Fluorescence with 1P and 2P Illumination", - "repository_type": "dandi", - "summary": "This dataset contains videos demonstrating the fluorescence of Human Embryonic Kidney (HEK) cells transfected with common genetically encoded voltage indicators (GEVIs). ", - "tags": [ - { - "id": 417, - "tag": "DANDI:000537" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 262, - "tag": "Homo sapiens - Human" - } - ], - "timestamp_created": "2023-12-18 16:51:37.997939+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000537/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "225": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 225, - "name": "Comparing the 1P and 2P Voltage Contrast of JEDI2P and Voltron2_JF525", - "repository_type": "dandi", - "summary": "This dataset demonstrates the voltage contrast of JEDI2P and Voltron2_JF525 under 1P and 2P illumination.", - "tags": [ - { - "id": 418, - "tag": "DANDI:000538" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 262, - "tag": "Homo sapiens - Human" - } - ], - "timestamp_created": "2023-12-18 16:51:39.313726+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000538/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "226": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230515.0530", - "id": 226, - "name": "Dataset for: A change in behavioral state switches the pattern of motor output that underlies rhythmic head and orofacial movements", - "repository_type": "dandi", - "summary": "Recorded multi-modal data (videography, respiration, electromyogram, wearable sensor signals, and human annotation) from rats performing naturalistic foraging and rearing behaviors in an open arena. Dataset for S.-M. Liao and D. Kleinfeld, Current Biology (2023). A change in behavioral state switches the pattern of motor output that underlies rhythmic head and orofacial movements. Dataset uploaded by the Kleinfeld Laboratory at University of California San Diego. Code can be found on https://rhythm-n-rodents.github.io/software/.", - "tags": [ - { - "id": 419, - "tag": "breathing" - }, - { - "id": 420, - "tag": "coupled oscillators" - }, - { - "id": 421, - "tag": "electromyogram" - }, - { - "id": 422, - "tag": "foraging" - }, - { - "id": 423, - "tag": "muscles" - }, - { - "id": 424, - "tag": "nose" - }, - { - "id": 425, - "tag": "preBotzinger complex" - }, - { - "id": 426, - "tag": "rearing" - }, - { - "id": 427, - "tag": "vibrissae" - }, - { - "id": 428, - "tag": "whiskers" - }, - { - "id": 429, - "tag": "neck" - }, - { - "id": 430, - "tag": "DANDI:000540" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 205, - "tag": "Rattus norvegicus - Norway rat" - } - ], - "timestamp_created": "2023-12-18 16:51:40.541660+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000540/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "227": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.241009.1457", - "id": 227, - "name": "NeuroPAL Microfluidic Chip Images and GCaMP activity", - "repository_type": "dandi", - "summary": "NeuroPAL Microfluidic chip images and GCaMP activity from \"NeuroPAL: A Multicolor Atlas for Whole-Brain Neuronal Identification in C. elegans\" and \"Extracting neural signals from semi-immobilized animals with deformable non-negative matrix factorization.\" ", - "tags": [ - { - "id": 431, - "tag": "DANDI:000541" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 394, - "tag": "Caenorhabditis elegans" - } - ], - "timestamp_created": "2023-12-18 16:51:41.745425+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000541/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "228": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230514.1148", - "id": 228, - "name": "Test Dataset", - "repository_type": "dandi", - "summary": "Test dataset for DataJoint uploads to DANDI", - "tags": [ - { - "id": 432, - "tag": "DANDI:000544" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:51:42.878663+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000544/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "229": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 229, - "name": "Test set", - "repository_type": "dandi", - "summary": "Another test upload for DataJoint", - "tags": [ - { - "id": 433, - "tag": "DANDI:000545" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:51:44.099015+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000545/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "230": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 230, - "name": "vStr_phase_stim", - "repository_type": "dandi", - "summary": "Extracellular ephys recording in the ventral Striatum", - "tags": [ - { - "id": 434, - "tag": "DANDI:000546" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:51:45.235148+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000546/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "231": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230822.1616", - "id": 231, - "name": "Perivascular Pumping of Cerebrospinal Fluid in the Brain with a Valve Mechanism", - "repository_type": "dandi", - "summary": "This dataset contains the 2-photon recordings of the penetrating arteries of mice, included in the project \"Perivascular Pumping of Cerebrospinal Fluid in the Brain with a Valve Mechanism\".", - "tags": [ - { - "id": 435, - "tag": "DANDI:000547" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:51:46.594261+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000547/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "232": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230519.1825", - "id": 232, - "name": "Effect of the number of pulses on electroporation by unipolar and 50 % bipolar pulses", - "repository_type": "dandi", - "summary": "Trains of 5, 10, or 15 pulses all at 1 Hz. Cells were electroporated by a train of 5, 10, or 15 unipolar 600 ns pulses (0 percent) or bipolar 600 + 600 ns pulses (50 percent) at 1 Hz. 50% represents the second pulse's amplitude in relation to the first pulse's amplitude. 0% represents a unipolar pulse, where no second pulse was applied. The electrodes were two parallel stainless-steel cylinders, 1.5 mm in diameter and 3 mm apart. Supported by NIH 1R21EY034258", - "tags": [ - { - "id": 436, - "tag": "DANDI:000548" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 409, - "tag": "Bos taurus - Cattle" - } - ], - "timestamp_created": "2023-12-18 16:51:47.738085+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000548/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "233": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230519.2043", - "id": 233, - "name": "Effect of the pulse duration on electroporation by unipolar and 50% bipolar pulses", - "repository_type": "dandi", - "summary": "Trains of 5 different duration pulses, all exposures were at 1 Hz. Cells were electroporated by a train of 5 unipolar 300ns, 1200ns, 50us, 100us, 500us or bipolar 300+300ns, 1200+1200ns, 50+50us, 100+100us, 500+500us pulses (second pulse's amplitude was 50% in relation to the first pulse's amplitude). The electrodes were two parallel stainless-steel cylinders, 1.5 mm in diameter and 3 mm apart. Supported by NIH 1R21EY034258", - "tags": [ - { - "id": 437, - "tag": "DANDI:000549" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 409, - "tag": "Bos taurus - Cattle" - } - ], - "timestamp_created": "2023-12-18 16:51:48.948689+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000549/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "234": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230520.1609", - "id": 234, - "name": "Pulse repetition rate", - "repository_type": "dandi", - "summary": "Effect of pulse repetition rate on electroporation by 5 600ns unipolar or 600+600ns bipolar pulses (second phase's amplitude was 50% in relation to the first pulse's amplitude), at 833kHz, 500kHz, 100kHz, 10kHz. The electrodes were two parallel stainless-steel cylinders, 1.5 mm in diameter and 3 mm apart. Supported by NIH 1R21EY034258\n", - "tags": [ - { - "id": 438, - "tag": "DANDI:000550" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 409, - "tag": "Bos taurus - Cattle" - } - ], - "timestamp_created": "2023-12-18 16:51:50.165243+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000550/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "235": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230630.2304", - "id": 235, - "name": "Preconfigured dynamics in the hippocampus are guided by embryonic birthdate and rate of neurogenesis", - "repository_type": "dandi", - "summary": "The incorporation of novel information into the hippocampal network is likely be constrained by its innate architecture and internally generated activity patterns. However, the origin, organization, and consequences of such patterns remain poorly understood. Here, we show that hippocampal network dynamics are affected by sequential neurogenesis. We birthdated CA1 pyramidal neurons with in-utero electroporation over 4 embryonic days encompassing the peak of hippocampal neurogenesis, and compared their functional features in freely moving, adult mice. Neurons of the same birthdate displayed distinct connectivity, coactivity across brain states, and assembly dynamics. Same birthdate hippocampal neurons were topographically organized, in that anatomically clustered (<500\u00b5m) neurons exhibited overlapping spatial representations. Overall, the wiring and functional features of CA1 pyramidal neurons reflected a combination of birthdate and the rate of neurogenesis. These observations demonstrate that sequential neurogenesis in embryonic development shapes the preconfigured forms of adult network dynamics.", - "tags": [ - { - "id": 439, - "tag": "Hippocampus" - }, - { - "id": 440, - "tag": "Neural circuits" - }, - { - "id": 441, - "tag": "Development of the nervous system" - }, - { - "id": 442, - "tag": "DANDI:000552" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:51:51.409405+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000552/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "236": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230531.1237", - "id": 236, - "name": "Human brain mapping with multithousand-channel PtNRGrids resolves spatiotemporal dynamics", - "repository_type": "dandi", - "summary": "This dataset contains recordings from PtNRGrids, which are devices designed to record electrocorticography (ECoG) activities from the brain surface at high-spatial resolution. PtNRGrids used here were a square-shaped array with 1024 channels and a 150-\u00b5m pitch, which was implanted to record neural activity from the entire right primary somatosensory barrel cortex. The sensory response on the brain was evoked by delivering air puffs through a microcapillary tube to individually stimulate individual whiskers on the contralateral side. This dataset demonstrates the high-spatial resolution recording capability of PtNRGrids isolate functional cortical columns in sub-mm resolution from the surface of the brain.\n\n[Publication corresponding to this dataset] Tchoe, Youngbin, et al. \"Human brain mapping with multithousand-channel PtNRGrids resolves spatiotemporal dynamics.\" Science translational medicine 14.628 (2022): eabj1441.\n[Electrode mapping information & Basic analysis codes] Github: https://ytchoe.github.io/", - "tags": [ - { - "id": 443, - "tag": "DANDI:000554" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 205, - "tag": "Rattus norvegicus - Norway rat" - } - ], - "timestamp_created": "2023-12-18 16:51:52.712008+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000554/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "237": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.240502.0456", - "id": 237, - "name": "Spontaneous behaviour is structured by reinforcement without explicit reward", - "repository_type": "dandi", - "summary": "Spontaneous animal behaviour is built from action modules that are concatenated by the brain into sequences. However, the neural mechanisms that guide the composition of naturalistic, self-motivated behaviour remain unknown. Here we show that dopamine systematically fluctuates in the dorsolateral striatum (DLS) as mice spontaneously express sub-second behavioural modules, despite the absence of task structure, sensory cues or exogenous reward. Photometric recordings and calibrated closed-loop optogenetic manipulations during open field behaviour demonstrate that DLS dopamine fluctuations increase sequence variation over seconds, reinforce the use of associated behavioural modules over minutes, and modulate the vigour with which modules are expressed, without directly influencing movement initiation or moment-to-moment kinematics. Although the reinforcing effects of optogenetic DLS dopamine manipulations vary across behavioural modules and individual mice, these differences are well predicted by observed variation in the relationships between endogenous dopamine and module use. Consistent with the possibility that DLS dopamine fluctuations act as a teaching signal, mice build sequences during exploration as if to maximize dopamine. Together, these findings suggest a model in which the same circuits and computations that govern action choices in structured tasks have a key role in sculpting the content of unconstrained, high-dimensional, spontaneous behavior.", - "tags": [ - { - "id": 444, - "tag": "Basal Ganglia" - }, - { - "id": 440, - "tag": "Neural circuits" - }, - { - "id": 445, - "tag": "Reward" - }, - { - "id": 446, - "tag": "DANDI:000559" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 262, - "tag": "Homo sapiens - Human" - } - ], - "timestamp_created": "2023-12-18 16:51:53.977911+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000559/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "238": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.241009.1504", - "id": 238, - "name": "C. elegans whole-brain neuroPAL and immobilized calcium imaging", - "repository_type": "dandi", - "summary": "All of the datasets here contain neuroPAL whole-brain structural images. A subset of these files also contain unstimulated calcium imaging data from the worm immobilized in a micro fluidic chip. Each dataset has ~30-50% of neuron centers labeled with cell IDs.", - "tags": [ - { - "id": 447, - "tag": "DANDI:000565" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 394, - "tag": "Caenorhabditis elegans" - } - ], - "timestamp_created": "2023-12-18 16:51:55.659727+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000565/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "239": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 239, - "name": "ASAP4 data", - "repository_type": "dandi", - "summary": "Raw data for \"A positively tuned voltage indicator for extended electrical recordings in the brain\", Nature Methods 2023, https://doi.org/10.1038/s41592-023-01913-z", - "tags": [ - { - "id": 448, - "tag": "DANDI:000566" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 274, - "tag": "Drosophila melanogaster - Fruit fly" - } - ], - "timestamp_created": "2023-12-18 16:51:56.807404+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000566/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "240": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230705.1633", - "id": 240, - "name": "Probing subthreshold dynamics of hippocampal neurons by pulsed optogenetics", - "repository_type": "dandi", - "summary": "Understanding how excitatory (E) and inhibitory (I) inputs are integrated by neurons requires monitoring their subthreshold behavior. We probed the subthreshold dynamics using optogenetic depolarizing pulses in hippocampal neuronal assemblies in freely moving mice. Excitability decreased during sharp- wave ripples coupled with increased I. In contrast to this \u201cnegative gain,\u201d optogenetic probing showed increased within-field excitability in place cells by weakening I and unmasked stable place fields in initially non\u2013place cells. Neuronal assemblies active during sharp-wave ripples in the home cage predicted spatial overlap and sequences of place fields of both place cells and unmasked preexisting place fields of non\u2013place cells during track running. Thus, indirect probing of subthreshold dynamics in neuronal populations permits the disclosing of preexisting assemblies and modes of neuronal operations.", - "tags": [ - { - "id": 449, - "tag": "DANDI:000568" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:51:57.916876+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000568/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "241": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230706.1630", - "id": 241, - "name": "20230630_AIBS_Patchseq_nonhuman_primate", - "repository_type": "dandi", - "summary": "HMBA Lein PatchSeq upload (Q2 2023)", - "tags": [ - { - "id": 27, - "tag": "Patch-seq" - }, - { - "id": 450, - "tag": "non-human primate" - }, - { - "id": 451, - "tag": "multimodal" - }, - { - "id": 452, - "tag": "DANDI:000569" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 453, - "tag": "Macaca nemestrina - Pigtail macaque" - } - ], - "timestamp_created": "2023-12-18 16:51:59.285199+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000569/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "242": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230706.1630", - "id": 242, - "name": "20230630_AIBS_Patchseq_human", - "repository_type": "dandi", - "summary": "U01 Lein Patch-seq data upload (Q2 2023)", - "tags": [ - { - "id": 27, - "tag": "Patch-seq" - }, - { - "id": 7, - "tag": "human" - }, - { - "id": 451, - "tag": "multimodal" - }, - { - "id": 454, - "tag": "DANDI:000570" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 262, - "tag": "Homo sapiens - Human" - } - ], - "timestamp_created": "2023-12-18 16:52:00.540907+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000570/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "243": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.250616.1143", - "id": 243, - "name": "Intracranial recordings using BCI2000 and the CorTec BrainInterchange", - "repository_type": "dandi", - "summary": "An Ecosystem of Technology and Protocols for Adaptive Neuromodulation Research in Humans\n\nThis study aims to develop an ecosystem for the purpose of neurmodulation using the Cortec BCI device and BCI2000 software.\n\nContact: For questions regarding this dataset, please contact mivalt.filip@mayo.edu or Miller.Kai@mayo.edu", - "tags": [ - { - "id": 455, - "tag": "DANDI:000571" - }, - { - "id": 181, - "tag": "DANDI" - } - ], - "timestamp_created": "2023-12-18 16:52:01.687813+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000571/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "244": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230826.0140", - "id": 244, - "name": "Activity map of a cortico-cerebellar loop underlying motor planning", - "repository_type": "dandi", - "summary": "Data from \"Activity map of a cortico-cerebellar loop underlying motor planning\" Jia Zhu, Hana Hasanbegovic, Liu D. Liu, Zhenyu Gao, Nuo Li. Nat Neurosci 2023\n\nAn activity map of the cerebellar cortex during a delayed response task in which mice used short-term memory to plan directional licking.\n\nSupported by Robert and Janice McNair Foundation, Whitehall Foundation, Alfred P. Sloan Foundation, Searle Scholars Program, Pew Scholars Program, NIH NS104781, NS112312, NS113110, McKnight Foundation, Simons Collaboration on the Global Brain.\n", - "tags": [ - { - "id": 456, - "tag": "DANDI:000572" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:52:02.996937+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000572/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "245": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.250610.0938", - "id": 245, - "name": "Dataset of human medial temporal lobe neurons, scalp and intracranial EEG during a verbal working memory task", - "repository_type": "dandi", - "summary": "We present an electrophysiological dataset recorded from nine subjects during a verbal working memory task. Subjects were epilepsy patients undergoing intracranial monitoring for localization of epileptic seizures. Subjects performed a modified Sternberg task in which the encoding of memory items, maintenance, and recall were temporally separated. The dataset includes simultaneously recorded scalp EEG with the 10-20 system, intracranial EEG (iEEG) recorded with depth electrodes, waveforms and spike times of 1526 units recorded in the medial temporal lobe, and the MNI coordinates and anatomical labels of all intracranial electrodes. Subject characteristics and information on sessions (set size, match/mismatch, correct/incorrect, response, response time for each trial) are also provided. This dataset enables the investigation of working memory by providing simultaneous scalp EEG and iEEG recordings, which can be used for connectivity analysis, alongside hard to obtain unit recordings from humans.", - "tags": [ - { - "id": 457, - "tag": "Electrophysiology" - }, - { - "id": 77, - "tag": "Human" - }, - { - "id": 458, - "tag": "Awake" - }, - { - "id": 459, - "tag": "Local field potential" - }, - { - "id": 460, - "tag": "Neuronal action potential" - }, - { - "id": 461, - "tag": "Spikes" - }, - { - "id": 462, - "tag": "Medial temporal lobe" - }, - { - "id": 439, - "tag": "Hippocampus" - }, - { - "id": 463, - "tag": "Entorhinal cortex" - }, - { - "id": 464, - "tag": "Amygdala" - }, - { - "id": 465, - "tag": "Scalp EEG" - }, - { - "id": 466, - "tag": "Intracranial EEG" - }, - { - "id": 467, - "tag": "Cognitive task" - }, - { - "id": 468, - "tag": "Verbal working memory" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 470, - "tag": "DANDI:000574" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 262, - "tag": "Homo sapiens - Human" - } - ], - "timestamp_created": "2023-12-18 16:52:04.240390+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000574/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "246": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.231010.1811", - "id": 246, - "name": "Dataset of human medial temporal lobe neurons during a visual working memory task", - "repository_type": "dandi", - "summary": "We present an electrophysiological dataset recorded from thirteen subjects during a visual working memory task. Subjects were epilepsy patients undergoing intracranial monitoring for localization of epileptic seizures. Here, we recorded single neuron firing in 13 epilepsy patients (7 male) while they performed a visual working memory task. The task is a change detection task designed to examine the visual working memory of subjects. In each trial, arrays of colored squares were presented and had to be memorized. The number of squares determined the set size: 1, 2, 4 or 6. There was a total 192 trials per session. Each trial started with a warning signal (0.4 s) that was a red fixation dot. The fixation dot was then changed to black (0.4 \u2013 0.5 s, jittered). A memory array (encoding period, 0.8 s) was followed by a delay (retention interval, 0.9 s). After the delay, a test array was shown (2 s) followed by a jittered inter-trial interval of 1.3 to 2.3 s. The participants indicated by button press (\u201cSame\u201d or \u201cDifferent\u201d, forced choice) whether the test array differed from the memory array. If the arrays differed, only one square changed in colour, but all squares remained on the same location. The fixation dot was visible on the screen during the whole trial period. Eight different colours were used for the memory and test array (yellow, red, green, blue, magenta, cyan, grey, black). Before starting the sessions, participants conducted trial runs in a practice session to learn the task. In this session we verified if participants were colour-blind and could discriminate all colours. Practice sessions were repeated until the participant understood the task and was able to follow the pace of the trials.", - "tags": [ - { - "id": 471, - "tag": "DANDI:000575" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 262, - "tag": "Homo sapiens - Human" - } - ], - "timestamp_created": "2023-12-18 16:52:05.421545+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000575/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "247": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.231010.1811", - "id": 247, - "name": "Dataset of neurons and intracranial EEG from human amygdala during aversive dynamic visual stimulation", - "repository_type": "dandi", - "summary": "We present an electrophysiological dataset collected from the amygdalae of nine subjects attending a visual dynamic stimulation of emotional aversive content. The subjects were patients affected by epilepsy who underwent preoperative invasive monitoring in the mesial temporal lobe. Subjects were presented with dynamic visual sequences of fearful faces (aversive condition), interleaved with sequences of neutral landscapes (neutral condition).\n\nWe provide simultaneous recordings of intracranial EEG (iEEG) and neuronal spike times and waveforms, and metadata related to the task, subjects, sessions and electrodes in the NIX standard. We technically validated this dataset and provide here the spike sorting quality metrics and the spectra of iEEG signals.\nThis dataset allows the investigation of amygdalar response to dynamic aversive stimuli at multiple spatial scales, from the macroscopic EEG to the neuronal firing in the human brain.", - "tags": [ - { - "id": 472, - "tag": "DANDI:000576" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 262, - "tag": "Homo sapiens - Human" - } - ], - "timestamp_created": "2023-12-18 16:52:06.523011+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000576/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "248": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230728.1727", - "id": 248, - "name": "Two-photon calcium imaging of mouse posterior cortical areas during dynamic navigation decisions (Tseng et al., 2022, Neuron)", - "repository_type": "dandi", - "summary": "This is the dataset for Tseng et al, \"Shared and specialized coding across posterior cortical areas for dynamic navigation decisions\" in Neuron (2022). The dataset contains calcium activity of >200,000 neurons recorded from 6 different cortical areas in mouse posterior cortex L2/3 and L5 using two-photon imaging, including V1 and secondary visual areas, retrosplenial cortex (RSC) and posterior parietal cortex (PPC), while the mice were performing a flexible decision-making task based on rule-switching during virtual navigation. There are total 300 behavior + imaging sessions collected from 8 mice. The neurons in each experiment have been registered into the Allen Institute Mouse Common Coordinate Framework (CCF) based on widefield retinotopy. In addition, these neurons contain fluorescent labels of retroAAV injected in one of the two sets of projection targets: an anterior part of anterior cingulate cortex/secondary motor cortex (ACC/M2) and striatum, or a posterior part of ACC/M2 and orbitofrontal cortex (OFC). \n\nPlease see the related paper for more details: https://doi.org/10.1016/j.neuron.2022.05.012\n\nAdditional resources:\n\n- Jupyter notebook for a tutorial to read and extract information from these NWB files: https://github.com/sytseng/Notebook_for_Dandiset_000579\n\n- NWB extension code for custom lab meta data (required for reading NWB files): https://github.com/sytseng/ndx-harvey-swac \n\n- Code and tutorials for fitting GLM to neural activity in Tensorflow 2: https://github.com/sytseng/GLM_Tensorflow_2", - "tags": [ - { - "id": 29, - "tag": "mouse" - }, - { - "id": 8, - "tag": "cortex" - }, - { - "id": 84, - "tag": "decision-making" - }, - { - "id": 473, - "tag": "navigation" - }, - { - "id": 474, - "tag": "virtual reality" - }, - { - "id": 475, - "tag": "two-photon imaging" - }, - { - "id": 476, - "tag": "posterior cortex" - }, - { - "id": 321, - "tag": "posterior parietal cortex" - }, - { - "id": 477, - "tag": "retrosplenial cortex" - }, - { - "id": 33, - "tag": "visual cortex" - }, - { - "id": 478, - "tag": "rule-switching" - }, - { - "id": 479, - "tag": "flexible decisions" - }, - { - "id": 480, - "tag": "retrograde labeling" - }, - { - "id": 481, - "tag": "DANDI:000579" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:52:07.777212+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000579/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "249": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 249, - "name": "Conjunctive Representation of Position, Direction, and Velocity in Entorhinal Cortex", - "repository_type": "dandi", - "summary": "The dataset includes spike times for recorded grid cells from the medial entorhinal cortex (MEC) in rats that explored two-dimensional environments. The behavioral data includes position from the tracking LEDs. \n\nThis sample was published in Sargolini et al. (Science, 2006).\n\nGrid cells in the medial entorhinal cortex (MEC) are part of an environment-independent spatial coordinate system. To determine how information about location, direction, and distance is integrated in the grid-cell network, we recorded from each principal cell layer of MEC in rats that explored two-dimensional environments. Whereas layer II was predominated by grid cells, grid cells colocalized with head-direction cells and conjunctive grid x head-direction cells in the deeper layers. All cell types were modulated by running speed. The conjunction of positional, directional, and translational information in a single MEC cell type may enable grid coordinates to be updated during self-motion-based navigation.\n", - "tags": [ - { - "id": 482, - "tag": "DANDI:000582" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 205, - "tag": "Rattus norvegicus - Norway rat" - } - ], - "timestamp_created": "2023-12-18 16:52:09.100049+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000582/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "250": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 250, - "name": "A Novel Neuropathic Pain Treatment: Achieving Neuronal Inhibition with a Splti Ring Resonator", - "repository_type": "dandi", - "summary": "Thesis defense data set consisting of extracellular action potentials and postsynaptic potentials captured in Procambarus clarkii.", - "tags": [ - { - "id": 483, - "tag": "DANDI:000615" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 484, - "tag": "Procambarus clarkii - Red swamp crayfish" - } - ], - "timestamp_created": "2023-12-18 16:52:10.209567+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000615/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "251": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 251, - "name": "SpikeForest ground truth datasets", - "repository_type": "dandi", - "summary": "The SpikeForest project contains a collection of electrophysiological recordings together with ground truth spiking information for the purpose of benchmarking the performance of spike sorting algorithms. In this dandiset we provide a subset of these recordings together with ground truth.\n\nThis dataset was prepared using the following script: https://github.com/flatironinstitute/spikeforest/blob/main/devel/dandiset/prepare_dandiset.py\n\nFor more information, see https://elifesciences.org/articles/55167", - "tags": [ - { - "id": 485, - "tag": "DANDI:000618" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:52:11.349193+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000618/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "252": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.240227.2023", - "id": 252, - "name": "Data for: Multimodal single-neuron, intracranial EEG, and fMRI brain responses during movie watching in human patients", - "repository_type": "dandi", - "summary": "We present a multimodal dataset of intracranial recordings, fMRI, and eye tracking in 20 participants during movie watching. Recordings consist of single neurons, local field potential, and intracranial EEG activity acquired from depth electrodes targeting the amygdala, hippocampus, and medial frontal cortex implanted for monitoring of epileptic seizures. Participants watched an 8-min long excerpt from the video \"Bang! You're Dead\" and performed a recognition memory test for movie content. 3\u2009T fMRI activity was recorded prior to surgery in 11 of these participants while performing the same task. This NWB- and BIDS-formatted dataset includes spike times, field potential activity, behavior, eye tracking, electrode locations, demographics, and functional and structural MRI scans. For technical validation, we provide signal quality metrics, assess eye tracking quality, behavior, the tuning of cells and high-frequency broadband power field potentials to familiarity and event boundaries, and show brain-wide inter-subject correlations for fMRI. This dataset will facilitate the investigation of brain activity during movie watching, recognition memory, and the neural basis of the fMRI-BOLD signal.\n\nThis dataset accompanies the following data descriptor: Keles, U., Dubois, J., Le, K.J.M., Tyszka, J.M., Kahn, D.A., Reed, C.M., Chung, J.M., Mamelak, A.N., Adolphs, R. and Rutishauser, U. Multimodal single-neuron, intracranial EEG, and fMRI brain responses during movie watching in human patients. Sci Data 11, 214 (2024). [Link to paper](https://doi.org/10.1038/s41597-024-03029-1)\n\nSample code to access and analyze this dataset has been provided: https://github.com/rutishauserlab/bmovie-release-NWB-BIDS", - "tags": [ - { - "id": 486, - "tag": "DANDI:000623" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 262, - "tag": "Homo sapiens - Human" - }, - { - "id": 4739, - "tag": "iEEG" - }, - { - "id": 4740, - "tag": "movie watching" - }, - { - "id": 4741, - "tag": "recognition memory" - }, - { - "id": 4742, - "tag": "single-neuron" - } - ], - "timestamp_created": "2023-12-18 16:52:12.470428+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000623/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "253": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 253, - "name": "A brainstem circuit for the expression of defensive facial reactions in rat", - "repository_type": "dandi", - "summary": "Chemoreceptors in the nasal epithelium can trigger an apneic reaction and a grimace in response to airborne irritants. Callado Perez et al. find that the underlying circuit does not involve olfaction. Rather, activation of neurons in the muralis subnucleus of the spinal trigeminal complex will inhibit the Pre-B\u00f6tzinger inhalation oscillator.", - "tags": [ - { - "id": 487, - "tag": "DANDI:000624" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 205, - "tag": "Rattus norvegicus - Norway rat" - } - ], - "timestamp_created": "2023-12-18 16:52:13.717460+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000624/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "254": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.231114.0019", - "id": 254, - "name": "Molecularly Identified CA1 Interneuron Dynamics", - "repository_type": "dandi", - "summary": "This dataset was collected to examine the dynamics of mouse interneurons whose subtypes were molecularly identified. 231 ROIs were observed over 3 separate imaging sessions. For each session you will find the following:\n- Raw images (individual cropped ROIs)\n- DfOverF traces\n- Fluorescence traces\n- Subtype of each ROI (some are undetermined)\n- Timing of water rewards\n- Time-stamped position of subject\n\n\nData was collected by Tristan Geiller at the Losonczy Lab using two photon calcium imaging and head-fixed mice running on a voluntary treadmill.", - "tags": [ - { - "id": 222, - "tag": "2-photon calcium imaging" - }, - { - "id": 34, - "tag": "interneuron" - }, - { - "id": 92, - "tag": "hippocampus" - }, - { - "id": 488, - "tag": "ca1" - }, - { - "id": 29, - "tag": "mouse" - }, - { - "id": 368, - "tag": "head-fixed" - }, - { - "id": 489, - "tag": "treadmill" - }, - { - "id": 490, - "tag": "Losonczy Lab" - }, - { - "id": 491, - "tag": "Columbia University" - }, - { - "id": 492, - "tag": "DANDI:000625" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:52:15.057207+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000625/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "255": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.240501.1912", - "id": 255, - "name": "Extracellular recording along macaque ventral visual pathway during natural image free viewing", - "repository_type": "dandi", - "summary": "Recordings spanning six areas in the macaque ventral visual pathway (V1, V2 ,V4, PIT, CIT, AIT; primarily in CIT and AIT) including 679 experimental sessions, 883 hours, 13 monkeys, 4.6 million fixations, and thousands of natural images. For more information, see the journal article: https://www.nature.com/articles/s41593-024-01631-5.\n\nFor example code using these files, see the GitHub repository linked below under Related resources, containing code to reprocude the paper analyses. Please note that the NWB files hosted here are organized somewhat differently from the code examples, so please adapt the code accordingly.\n\nTo interactively explore the NWB files, a useful tool is HDFView (https://www.hdfgroup.org/downloads/hdfview/).", - "tags": [ - { - "id": 78, - "tag": "Macaque" - }, - { - "id": 493, - "tag": "Ventral stream" - }, - { - "id": 494, - "tag": "Free viewing" - }, - { - "id": 495, - "tag": "Natural images" - }, - { - "id": 496, - "tag": "Extracellular electrophysiology" - }, - { - "id": 497, - "tag": "DANDI:000628" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 210, - "tag": "Macaca mulatta - Rhesus monkey" - } - ], - "timestamp_created": "2023-12-18 16:52:16.281150+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000628/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "256": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.240627.0646", - "id": 256, - "name": "Gillespie et al (2024) Neurofeedback training can modulate task-relevant memory replay rate in rats", - "repository_type": "dandi", - "summary": "This dataset includes the electrophysiological (dorsal CA1 tetrodes) and behavioral (port triggers, reward delivery, and position tracking) data from the 4 subjects in the neurofeedback cohort described in Gillespie et al, eLife 2024: \"Neurofeedback training can modulate task-relevant memory replay rate in rats\". The data for the 4 control cohort subjects can be found in Dandiset 000115 (https://dandiarchive.org/dandiset/000115). For more information about this data, please contact Anna Gillespie.", - "tags": [ - { - "id": 498, - "tag": "DANDI:000629" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 205, - "tag": "Rattus norvegicus - Norway rat" - }, - { - "id": 35, - "tag": "electrophysiology" - }, - { - "id": 92, - "tag": "hippocampus" - }, - { - "id": 4744, - "tag": "neurofeedback" - }, - { - "id": 4745, - "tag": "replay" - }, - { - "id": 4746, - "tag": "sharp-wave ripples" - } - ], - "timestamp_created": "2023-12-18 16:52:17.487359+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000629/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "257": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230915.2257", - "id": 257, - "name": "Human L1 patch-seq electrophysiology", - "repository_type": "dandi", - "summary": "Patch-seq electrophysiology data from human neocortical L1 neurons, accompanying the manuscript \"Morpho-electric and transcriptomic divergence of the layer 1 interneuron repertoire in human versus mouse neocortex\" (https://www.biorxiv.org/content/10.1101/2022.10.24.511199).\n\nAnalysis code and extracted features available at https://github.com/AllenInstitute/patchseq_human_L1. Feature extraction package available at https://github.com/AllenInstitute/ipfx.", - "tags": [ - { - "id": 7, - "tag": "human" - }, - { - "id": 451, - "tag": "multimodal" - }, - { - "id": 499, - "tag": "patch-seq" - }, - { - "id": 40, - "tag": "neocortex" - }, - { - "id": 500, - "tag": "DANDI:000630" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 77, - "tag": "Human" - } - ], - "timestamp_created": "2023-12-18 16:52:18.825558+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000630/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "258": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.231012.1508", - "id": 258, - "name": "Effect of the electric field vector change on the electroporation efficiency of paired-pulse trains compared to single-pulse trains", - "repository_type": "dandi", - "summary": "In the study, the impact of varying the electric field vector on the effectiveness of electroporation was examined, utilizing both paired-pulse trains, consisting of 5 pulses at 600 + 600 ns, and single-pulse trains with 5 pulses at 600 ns. These were applied to a BPAE cell monolayer at frequencies of either 1 Hz or 833 kHz. The focus was on assessing the alterations in electroporation efficiency, gauged through the changes in YoPro-1 emission when compared to the baseline established by a single-pulse train in the identical region of interest, which was set at 100%. The evaluation criteria included the angle at which the electric field vector direction alternated between two pulses within a pair. Introduction of pulses in pairs amplified the electroporation efficiency at smaller angles but resulted in an inhibition at larger angles, indicating a frequency-dependent relationship. The images captured during the experiments provided a comprehensive visualization of this effect, highlighting the nuanced impact of electric field vector modifications on electroporation efficiency. Supported by NIH 1R21EY034258", - "tags": [ - { - "id": 501, - "tag": "DANDI:000631" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 409, - "tag": "Bos taurus - Cattle" - } - ], - "timestamp_created": "2023-12-18 16:52:20.257070+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000631/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "259": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.231012.1930", - "id": 259, - "name": "Electroporation efficiency of co-directional and cross-directional paired pulses", - "repository_type": "dandi", - "summary": "The efficiency of electroporation was evaluated in an experiment involving co-directional and cross-directional paired pulses using a triangular electrode array. BPAE cell monolayers were exposed to trains of four paired pulses, each consisting of 600 + 600 ns, at two distinct frequencies: 1 Hz and 770 kHz, with an amplitude of 10 kV/cm. In one scenario, the electric field direction remained consistent throughout each pair (co-directional). In another, the field direction was altered by 90\u00b0 (cross-directional). The electroporation efficiency was quantified through the cells\u2019 uptake of YoPro-1 dye, serving as a measure of the permeabilization of the cell membranes under varying electric field orientations and frequencies. Supported by NIH 1R21EY034258\n", - "tags": [ - { - "id": 502, - "tag": "DANDI:000632" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 409, - "tag": "Bos taurus - Cattle" - } - ], - "timestamp_created": "2023-12-18 16:52:21.541780+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000632/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "260": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.231013.2226", - "id": 260, - "name": "The difference in electroporation patterns produced by a train of single pulses and a train of paired pulses", - "repository_type": "dandi", - "summary": "The difference in electroporation patterns produced by a train of single pulses and a train of paired pulses. Supported by NIH 1R21EY034258", - "tags": [ - { - "id": 503, - "tag": "DANDI:000633" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 409, - "tag": "Bos taurus - Cattle" - } - ], - "timestamp_created": "2023-12-18 16:52:22.725123+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000633/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "261": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.231005.1820", - "id": 261, - "name": "Cell Membrane Charging by Co- and Counter-Directional ns electrical pulses (nsEP)", - "repository_type": "dandi", - "summary": "Strobe photography, used at nanosecond resolution, captures the dynamic process of cell membrane charging and relaxation kinetics in cells treated with FluoVolt dye and exposed to electric pulses. This study uses a triangular electrode array where 300 ns pulses are applied alternately at a frequency of 1.67 MHz. The precise coordination of electric pulses and laser flashes, adjusted in 50 ns steps, allows for the detailed imaging of the cell's response. The electric field induces enhanced fluorescence at the cathode-facing side of the FluoVolt-loaded CHO cells and suppressed fluorescence at the anode-facing side. This research method provides a comprehensive and reliable examination of the cell membrane's behavior under electrical stimulation without causing damage, ensuring consistent observations to understand cellular responses to varying electrical stimuli. Supported by NIH 1R21EY034258", - "tags": [ - { - "id": 504, - "tag": "DANDI:000634" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 376, - "tag": "Cricetulus griseus - Cricetulus aureus" - } - ], - "timestamp_created": "2023-12-18 16:52:23.976330+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000634/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "262": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.230921.1734", - "id": 262, - "name": "20230930_AIBS_Patchseq_nonhuman_primate", - "repository_type": "dandi", - "summary": "HMBA Lein PatchSeq upload (Q3 2023)", - "tags": [ - { - "id": 27, - "tag": "Patch-seq" - }, - { - "id": 450, - "tag": "non-human primate" - }, - { - "id": 451, - "tag": "multimodal" - }, - { - "id": 505, - "tag": "DANDI:000635" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 506, - "tag": "Macaca nemestrina" - } - ], - "timestamp_created": "2023-12-18 16:52:25.293015+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000635/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "263": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.241120.0510", - "id": 263, - "name": "Human interneuron patch-seq electrophysiology", - "repository_type": "dandi", - "summary": "Patch-seq electrophysiology data from human neocortical interneurons from acute and slice culture preparations, accompanying the manuscript \"Signature morpho-electric properties of diverse GABAergic interneurons in the human neocortex\" (https://www.biorxiv.org/content/10.1101/2022.11.08.515739).", - "tags": [ - { - "id": 7, - "tag": "human" - }, - { - "id": 451, - "tag": "multimodal" - }, - { - "id": 499, - "tag": "patch-seq" - }, - { - "id": 40, - "tag": "neocortex" - }, - { - "id": 507, - "tag": "DANDI:000636" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 77, - "tag": "Human" - } - ], - "timestamp_created": "2023-12-18 16:52:26.587360+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000636/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "264": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.250711.1944", - "id": 264, - "name": "Neural Spike Time Response Data in Anesthetized Rats in the Primary Somatosensory Cortex with Phased Ultrasound Array Stimulation", - "repository_type": "dandi", - "summary": "In this study, we investigate the neuronal response to transcranial focused ultrasound (tFUS) stimulation on somatosensory cortex by using a 128-element array transducer. Intracranial recordings were performed using a 32-channel Neuronexus multi-electrode array from somatosensory cortex. Wistar rats were anesthetized using isoflurane at 2%. tFUS with different parameters was applied every 2.5s (\u00b110%). Data was recorded using a TDT system and MUA recordings were sorted using the Plexon Offline Sorter to determine the spike times.This dataset contains spike times of the sorted neural signals and event time for each tFUS trial. In these recordings, we test different pulse repetition frequencies (PRFs) (30Hz, 300Hz, 1500Hz, 3000Hz, and 4500Hz), ultrasound durations (UDs) (100\u00b5s, 200\u00b5s, and 400\u00b5s) of ultrasound stimulation to explore the neuronal response to tFUS. The pulse duration was kept at 67ms. Each recording has 500 trials. Detailed description about this dataset can be found in the following publication. Please cite the paper if you would use a portion of the dataset. Gao, H., Ramachandran, S., Yu, K., & He, B. (2025). Transcranial Focused Ultrasound Modulates Feedforward and Feedback Cortico-Thalamo-Cortical Pathways by Selectively Activating Excitatory Neurons. The Journal of Neuroscience, 45(23), e2218242025. https://doi.org/10.1523/JNEUROSCI.2218-24.2025", - "tags": [ - { - "id": 508, - "tag": "DANDI:000637" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 205, - "tag": "Rattus norvegicus - Norway rat" - } - ], - "timestamp_created": "2023-12-18 16:52:27.960721+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000637/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "265": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.231108.1843", - "id": 265, - "name": "32-CH Local Field Potential Data During Probabilistic Reversal Learning Task", - "repository_type": "dandi", - "summary": "Male Long Evans Rats 7-12 months of age. 24 total animals. 256 sessions. 13 animals received a frontal traumatic brain injury (controlled cortical impact). 11 animals received Sham (no craniotomy no TBI) conditions. One week later, rats underwent a second surgery to implant 32 electrodes (Francoeur et al., Front. Psychiatry, 2021). Rats were trained to perform a self-paced probabilistic reversal learning task in a custom operant box with five noseports, stepper-motors to deliver water reward, houselight, and auditory tones. Local field potential (LFP) streams recorded simultaneously from 32 Brain Regions - recorded using a 32-Channel RHD headstage coupled to a RHD USB interface board (Intan Technologies). Open Ephys software at 1KHz band-pass filter set at 0.3-999Hz during acquisition. Lab-streaming-layer (LSL) was used to integrate physiology data with behavior. Physiology can be time-locked to trial onset (rat responds to start trial), response (rat choice between noseports), reward (time of reward delivery)", - "tags": [ - { - "id": 35, - "tag": "electrophysiology" - }, - { - "id": 509, - "tag": "rodent behavior" - }, - { - "id": 510, - "tag": "reversal learning" - }, - { - "id": 511, - "tag": "traumatic brain injury" - }, - { - "id": 512, - "tag": "DANDI:000640" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 205, - "tag": "Rattus norvegicus - Norway rat" - } - ], - "timestamp_created": "2023-12-18 16:52:29.290993+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000640/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "266": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 266, - "name": "Volumetric multiplex imaging of whole human and non-human primate brains", - "repository_type": "dandi", - "summary": "4mm SHIELD-processed human, macaque, and marmoset brain coronal sections, multi-round stained with DNA-labeled antibodies and Fluorescence In Situ Hybridization probes, and imaged with oblique fluorescence light sheet microscopes.", - "tags": [ - { - "id": 513, - "tag": "DANDI:000674" - }, - { - "id": 181, - "tag": "DANDI" - } - ], - "timestamp_created": "2023-12-18 16:52:30.510510+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000674/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "267": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.231004.2146", - "id": 267, - "name": "Pupil and movement measurements during mouse auditory and visual discrimination", - "repository_type": "dandi", - "summary": "Pupil diameter, face motion energy, and locomotion speed measures recorded during sensory discrimination behavior of head-fixed mice. Subjects classify the frequency of an auditory tone cloud stimulus, or the angle of a drifting Gabor patch, at right or left lick ports. Data include 391 behavioral sessions from 13 subjects. For more information see the preprint at https://www.biorxiv.org/content/10.1101/2023.03.02.530651v2.", - "tags": [ - { - "id": 514, - "tag": "DANDI:000678" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:52:31.641819+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000678/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "268": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 268, - "name": "similarity-weighted interleaved learning", - "repository_type": "dandi", - "summary": "High-density silicon probe recordings from dorsal and intermediate CA1, primary visual cortex, and antero-lateral visual cortex in mice while they are exploring VR environments with different levels of similarity. The experiment was aimed at studying the content of replay across superficial and deep layers of visual cortex. ", - "tags": [ - { - "id": 515, - "tag": "cortex layers" - }, - { - "id": 92, - "tag": "hippocampus" - }, - { - "id": 57, - "tag": "learning" - }, - { - "id": 516, - "tag": "memory" - }, - { - "id": 517, - "tag": "memory replay" - }, - { - "id": 518, - "tag": "memory consolidation" - }, - { - "id": 519, - "tag": "DANDI:000687" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:52:32.996888+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000687/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "269": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 269, - "name": "An optical design enabling lightweight and large field-of-view head-mounted microscopes", - "repository_type": "dandi", - "summary": "Here we present a fluorescence microscope light path that enables imaging, during free behavior, of thousands of neurons in mice and hundreds of neurons in juvenile songbirds. The light path eliminates traditional illumination optics, allowing for head-mounted microscopes that have both a lower weight and a larger field of view (FOV) than previously possible. Using this light path, we designed two microscopes: one optimized for FOV (~4 mm FOV; 1.4 g), and the other optimized for weight (1.0 mm FOV; 1.0 g). \nThis dataset includes the calcium imaging data from our microscope optimized for weight. For this experiment, we stereotactically located a region of the mouse brain at coordinates associatied with primary visual and somatosensory regions, performed approximately 20 viral injections of GCaMP7f across a 4-mm-diameter region of cortex, and then implanted a 4-mm-diameter glass window in the skull. After letting each mouse recover, we installed our head-mounted microscope and allowed it to freely explore a circular maze while recording calcium activity for several minutes.", - "tags": [ - { - "id": 520, - "tag": "DANDI:000691" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - }, - { - "id": 24, - "tag": "NWB" - } - ], - "timestamp_created": "2023-12-18 16:52:34.280726+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000691/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "270": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.240402.2118", - "id": 270, - "name": "Whole-brain spontaneous GCaMP activity with NeuroPAL cell ID information of semi-restricted worms", - "repository_type": "dandi", - "summary": "Spontaneous neuronal activities were extracted from GCaMP and tagRFP images, covering nearly all neurons in the nematode C. elegans brain. Cell segmentation and tracking were performed using 3DeeCellTracker (https://github.com/WenChentao/3DeeCellTracker). Neuronal identity information was derived from NeuroPAL signals with the NeuroPAL Auto-ID software (https://www.yeminilab.com/neuropal) and manual corrections. During the data acquisition, worms were semi-restricted in a microfluidic device (https://www.nature.com/articles/nature06292). Note: There are occasional transient failures in neuronal tracking. We recommend researchers to use a moving median filter (e.g., \u00b13) on the time series data to address this issue.", - "tags": [ - { - "id": 521, - "tag": "DANDI:000692" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 394, - "tag": "Caenorhabditis elegans" - } - ], - "timestamp_created": "2023-12-18 16:52:35.430267+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000692/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "271": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 271, - "name": "The organization of context versus content coding in the hippocampus and neocortex", - "repository_type": "dandi", - "summary": "High-density silicon probe recordings from primary somatosensory cortex, posterior parietal cortex, and dorsal hippocampus (CA1) in head-fixed mice while running on a treadmill virtual reality task with different tactile cues.", - "tags": [ - { - "id": 522, - "tag": "DANDI:000696" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:52:36.636699+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000696/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "272": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 272, - "name": "Laminar coding properties of visual object representations in the mouse neocortex across multiple contexts", - "repository_type": "dandi", - "summary": "High-density silicon probe recordings in the visual and parietal cortices during visual object recognition in the mouse.", - "tags": [ - { - "id": 523, - "tag": "DANDI:000710" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:52:37.784135+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000710/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "273": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.231121.1730", - "id": 273, - "name": "Allen Institute - Visual Behavior - Optical Physiology", - "repository_type": "dandi", - "summary": "The Visual Behavior Ophys dataset was generated using in vivo 2-photon calcium imaging (also called optical physiology, or \u201cophys\u201d) to measure the activity of genetically identified neurons in the visual cortex of mice performing a go/no-go visual change detection task. Population of neurons were recorded over multiple days with varying sensory and behavioral contexts, including familiar and novel stimuli, and passive exposure sessions. This dataset can be used to evaluate the influence of experience, expectation, and task engagement on neural coding and dynamics in excitatory, Vip inhibitory, and Sst inhibitory cell populations. \n\nThe full ophys dataset includes neural and behavioral measurements from 107 well-trained mice during 703 in vivo 2-photon imaging sessions from 326 unique fields of view, resulting in a total of 50,476 cortical neurons recorded. The full behavioral training history of all imaged mice is also provided as part of the dataset, allowing investigation into task learning, behavioral strategy, and inter-animal variability. There are a total of 4,782 behavior sessions available for analysis.\n\nFull documentation of the Visual Behavior Ophys dataset and tutorials can be found here: https://allensdk.readthedocs.io/en/latest/visual_behavior_optical_physiology.html\n\nThe recommended route for interacting with these data is through the AllenSDK (https://github.com/AllenInstitute/AllenSDK/) which provides methods for downloading and processing data. Documentation of the AllenSDK can be found here: https://allensdk.readthedocs.io/en/latest/. \n\nThere are several metadata summary tables that are available as \"related resources\" associated with this DANDI-set. You can also use the AllenSDK to download these tables. See this notebook (https://allensdk.readthedocs.io/en/latest/_static/examples/nb/visual_behavior_ophys_data_access.html) for information on downloading the metadata and the associated NWB files.\n\nPublicly available pre-prints using this dataset include: \n\nStimulus novelty uncovers coding diversity in visual cortical circuits\nhttps://doi.org/10.1101/2023.02.14.528085 \n\nBehavioral strategy shapes activation of the Vip-Sst disinhibitory circuit in visual cortex\nhttps://doi.org/10.1101/2023.04.28.538575 \n\nFamiliarity modulated synapses model visual cortical circuit novelty responses\nhttps://doi.org/10.1101/2023.08.16.553635 ", - "tags": [ - { - "id": 29, - "tag": "mouse" - }, - { - "id": 33, - "tag": "visual cortex" - }, - { - "id": 524, - "tag": "2-photon microscopy" - }, - { - "id": 525, - "tag": "calcium imaging" - }, - { - "id": 526, - "tag": "excitatory neurons" - }, - { - "id": 527, - "tag": "inhibitory neurons" - }, - { - "id": 528, - "tag": "novelty" - }, - { - "id": 529, - "tag": "task engagement" - }, - { - "id": 530, - "tag": "behavior" - }, - { - "id": 57, - "tag": "learning" - }, - { - "id": 531, - "tag": "change detection" - }, - { - "id": 532, - "tag": "disinhibition" - }, - { - "id": 533, - "tag": "DANDI:000711" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:52:39.228712+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000711/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "274": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.240702.1725", - "id": 274, - "name": "Allen Institute - Visual Behavior - Neuropixels", - "repository_type": "dandi", - "summary": "Data released here is for archival purposes. The recommended route for interacting with these data is through the AllenSDK (https://github.com/AllenInstitute/AllenSDK/) which provides methods for downloading and processing data. Documentation of the AllenSDK can be found here: https://allensdk.readthedocs.io/en/latest/. Full documentation of the Visual Behavior Neuropixels dataset and tutorials can be found here: https://allensdk.readthedocs.io/en/latest/visual_behavior_neuropixels.html ", - "tags": [ - { - "id": 534, - "tag": "DANDI:000713" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:52:40.544691+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000713/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "275": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.241009.1516", - "id": 275, - "name": "Segmented and labeled NeuroPAL structural images", - "repository_type": "dandi", - "summary": "Segmented and labeled NeuroPAL datasets from \"Graphical-model framework for automated annotation of cell identities in dense cellular images\" by Shivesh Chaudhary, et al.", - "tags": [ - { - "id": 535, - "tag": "DANDI:000714" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 394, - "tag": "Caenorhabditis elegans" - } - ], - "timestamp_created": "2023-12-18 16:52:41.762591+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000714/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "276": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 276, - "name": "NWB Cloud Benchmarks - HDF5", - "repository_type": "dandi", - "summary": "This dandiset is a collection of derived and generated assets used for rigorously assessing file configurations and their impact on performance in cloud environments.", - "tags": [ - { - "id": 536, - "tag": "DANDI:000717" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - }, - { - "id": 4775, - "tag": "benchmarking" - }, - { - "id": 4776, - "tag": "cloud" - }, - { - "id": 4777, - "tag": "performance" - } - ], - "timestamp_created": "2023-12-18 16:52:42.990887+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000717/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "277": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 277, - "name": "Offline ensemble co-reactivation links memories across days", - "repository_type": "dandi", - "summary": "Memories are encoded in neural ensembles during learning and are\u00a0stabilized by post-learning reactivation. Integrating recent experiences into existing memories ensures that memories contain the most recently available information, but how the brain accomplishes this critical process remains unclear. Here we show that in mice, a strong aversive experience drives offline ensemble reactivation of not only the recent aversive memory but also a neutral memory formed 2 days before, linking fear of the recent aversive memory to the previous neutral memory. Fear specifically links retrospectively, but not prospectively, to neutral memories across days. Consistent with previous studies, we find that the recent aversive memory ensemble is reactivated during the offline period after learning. However, a strong aversive experience also increases co-reactivation of the aversive and neutral memory ensembles during the offline period. Ensemble co-reactivation occurs more during wake than during sleep. Finally, the expression of fear in the neutral context is associated with reactivation of the shared ensemble between the aversive and neutral memories. Collectively, these results demonstrate that offline ensemble co-reactivation is a neural mechanism by which memories are integrated across days.", - "tags": [ - { - "id": 537, - "tag": "DANDI:000718" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - }, - { - "id": 4778, - "tag": "aversive" - }, - { - "id": 4779, - "tag": "co-activity" - }, - { - "id": 4780, - "tag": "ensemble" - }, - { - "id": 92, - "tag": "hippocampus" - }, - { - "id": 57, - "tag": "learning" - }, - { - "id": 516, - "tag": "memory" - }, - { - "id": 4781, - "tag": "memory-linking" - }, - { - "id": 4782, - "tag": "offline" - }, - { - "id": 4783, - "tag": "reactivation" - } - ], - "timestamp_created": "2023-12-18 16:52:44.228363+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000718/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "278": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "draft", - "id": 278, - "name": "NWB Cloud Benchmarks - Zarr", - "repository_type": "dandi", - "summary": "This dandiset is reserved by CatalystNeuro and will be published at a future point in time.", - "tags": [ - { - "id": 538, - "tag": "DANDI:000719" - }, - { - "id": 181, - "tag": "DANDI" - } - ], - "timestamp_created": "2023-12-18 16:52:45.351100+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000719/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "279": { - "auto_sync": true, - "content_types": "experimental", - "content_types_list": [ - "experimental" - ], - "default_context": "0.240827.1809", - "id": 279, - "name": "Allen Institute - Visual Coding - Optical Physiology", - "repository_type": "dandi", - "summary": "From the Allen Institute Brain Observatory, the Visual Coding (optical physiology) dataset is a large-scale, standardized survey of physiological activity across the mouse visual cortex, hippocampus, and thalamus. This two-photon imaging dataset features visually evoked calcium responses from GCaMP6-expressing neurons in a range of cortical layers, visual areas, and Cre lines. We hope that experimentalists and modelers will use these comprehensive, open datasets as a testbed for theories of visual information processing.\n\nFull documentation of the Visual Coding Ophys dataset and tutorials can be found here: https://observatory.brain-map.org/visualcoding/\n\nThe Visual Coding Ecephys (NeuroPixels) dataset can be found here: https://dandiarchive.org/dandiset/000021\n\n", - "tags": [ - { - "id": 539, - "tag": "DANDI:000728" - }, - { - "id": 181, - "tag": "DANDI" - }, - { - "id": 24, - "tag": "NWB" - }, - { - "id": 194, - "tag": "Mus musculus - House mouse" - } - ], - "timestamp_created": "2023-12-18 16:52:46.600148+00:00", - "timestamp_updated": "---", - "uri": "https://dandiarchive.org/dandiset/000728/draft", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "280": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 280, - "name": "Primary Auditory Cortex network", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 541, - "tag": "Pyramidal cell" - }, - { - "id": 542, - "tag": "neuroConstruct" - }, - { - "id": 543, - "tag": "Auditory system" - }, - { - "id": 544, - "tag": "Detailed cell model" - }, - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 602, - "tag": "Network model" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2023-12-18 17:56:01.965658+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/ACnet2", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "281": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 281, - "name": "Allen Institute & NeuroML", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 545, - "tag": "Large scale brain initiative" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 604, - "tag": "Network" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 605, - "tag": "SWC & others" - } - ], - "timestamp_created": "2023-12-18 18:31:54.605116+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/AllenInstituteNeuroML", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "282": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 282, - "name": "L5 Pyramidal Neuron - Almog and Korngreen 2014", - "repository_type": "github", - "summary": "Conversion to neuroConstruct and NeuroML of the L5 Pyramidal cell model described in:\n\nAlmog M, Korngreen A (2014) [A Quantitative Description of Dendritic Conductances and Its Application to Dendritic Excitation in Layer 5 Pyramidal Neurons](http://www.jneurosci.org/content/34/1/182) J Neurosci 34(1):182-196\n", - "tags": [ - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 541, - "tag": "Pyramidal cell" - }, - { - "id": 544, - "tag": "Detailed cell model" - }, - { - "id": 542, - "tag": "neuroConstruct" - }, - { - "id": 546, - "tag": "Goldman-Hodgkin-Katz current" - }, - { - "id": 606, - "tag": "L5 pyramidal cell" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2023-12-18 18:45:43.049107+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/korngreen-pyramidal", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "283": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 283, - "name": "Arbor Showcase", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 547, - "tag": "Arbor" - }, - { - "id": 548, - "tag": "Showcase" - } - ], - "timestamp_created": "2023-12-18 18:45:43.894933+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/ArborShowcase", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "284": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 284, - "name": "Reduced L5 Pyramidal Cell - Bahl et al. 2012 ", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 607, - "tag": "Layer 5 Pyramidal cell" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2023-12-18 18:45:44.485582+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/BahlEtAl2012_ReducedL5PyrCell", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "285": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 285, - "name": "Blender to NeuroML for C elegans connectome", - "repository_type": "github", - "summary": "Test of Blender to NeuroML conversion\n", - "tags": [ - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 549, - "tag": "OpenWorm" - }, - { - "id": 608, - "tag": "Blender" - }, - { - "id": 559, - "tag": "C. elegans" - }, - { - "id": 609, - "tag": "Nervous system" - }, - { - "id": 602, - "tag": "Network model" - } - ], - "timestamp_created": "2023-12-18 18:45:44.995820+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/openworm/Blender2NeuroML", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "286": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 286, - "name": "Blue Brain Project Showcase", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 544, - "tag": "Detailed cell model" - }, - { - "id": 541, - "tag": "Pyramidal cell" - }, - { - "id": 550, - "tag": "Neuronal reconstruction" - }, - { - "id": 545, - "tag": "Large scale brain initiative" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 604, - "tag": "Network" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2023-12-18 18:45:45.496755+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/BlueBrainProjectShowcase", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "287": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 287, - "name": "Sparsely connected spiking neuron network - Brunel 2000", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 551, - "tag": "Point neuron network" - }, - { - "id": 552, - "tag": "Integrate and fire neuron" - }, - { - "id": 610, - "tag": "Generic" - }, - { - "id": 611, - "tag": "NEST" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 602, - "tag": "Network model" - } - ], - "timestamp_created": "2023-12-18 18:45:45.990102+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/Brunel2000", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "288": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 288, - "name": "c302", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 549, - "tag": "OpenWorm" - }, - { - "id": 559, - "tag": "C. elegans" - }, - { - "id": 609, - "tag": "Nervous system" - }, - { - "id": 602, - "tag": "Network model" - }, - { - "id": 612, - "tag": "NeuroML2" - } - ], - "timestamp_created": "2023-12-18 18:45:46.486166+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/openworm/c302", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "289": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 289, - "name": "CA1 Oriens Lacunosum Moleculare - Lawrence et al. 2006", - "repository_type": "github", - "summary": "CA1 Oriens Lacunosum Moleculare multi-compartment model:\r\nLawrence JJ, Saraga F, Churchill JF, Statland JM, Travis KE, Skinner FK, McBain CJ (2006) Somatodendritic Kv7-KCNQ-M channels control interspike interval in hippocampal interneurons. J Neurosci 26:12325-38", - "tags": [ - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 553, - "tag": "Hippocampal formation" - }, - { - "id": 554, - "tag": "CA1" - }, - { - "id": 555, - "tag": "Interneuron" - }, - { - "id": 439, - "tag": "Hippocampus" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 613, - "tag": "Oriens Lacunosum Moleculare" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2023-12-18 18:45:47.281102+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/agmccrei/CA1-Oriens-Lacunosum-Moleculare---Lawrence-et-al.-2006", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "290": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 290, - "name": "CA1 Oriens Lacunosum Moleculare - Saraga et al. 2003", - "repository_type": "github", - "summary": "CA1 Oriens Lacunosum Moleculare multi-compartment model:\r\nSaraga F, Wu CP, Zhang L, Skinner FK (2003) Active Dendrites and Spike Propagation in Multi-compartment Models of Oriens-Lacunosum/Moleculare Hippocampal Interneurons. J Physiol 552(3):673-689", - "tags": [ - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 553, - "tag": "Hippocampal formation" - }, - { - "id": 554, - "tag": "CA1" - }, - { - "id": 544, - "tag": "Detailed cell model" - }, - { - "id": 439, - "tag": "Hippocampus" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 613, - "tag": "Oriens Lacunosum Moleculare" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2023-12-21 18:21:01.612075+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/agmccrei/CA1-Oriens-Lacunosum-Moleculare---Saraga-et-al.-2003", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "292": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 292, - "name": "CA1 pyramidal cell - Ferguson et al. 2014", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 553, - "tag": "Hippocampal formation" - }, - { - "id": 554, - "tag": "CA1" - }, - { - "id": 556, - "tag": "Izhikevich neuron model" - }, - { - "id": 647, - "tag": "Brain" - }, - { - "id": 614, - "tag": "CA1 Pyramidal cell" - }, - { - "id": 439, - "tag": "Hippocampus" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2023-12-21 18:21:03.253179+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/FergusonEtAl2014-CA1PyrCell", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "293": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 293, - "name": "CA1 Pyramidal Cell - Migliore et al. 2005", - "repository_type": "github", - "summary": "![](/attachments/download/1/CA1-spike.PNG)\r\nConversion of [hippocampal CA1 pyramidal cell](http://neurolex.org/wiki/Category:Hippocampus_CA1_pyramidal_cell) from [Migliore et al 2005](http://senselab.med.yale.edu/ModelDB/ShowModel.asp?model=55035).\r\n\r\n[![Build Status](https://travis-ci.org/OpenSourceBrain/CA1PyramidalCell.svg?branch=master)](https://travis-ci.org/OpenSourceBrain/CA1PyramidalCell)\r\n", - "tags": [ - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 553, - "tag": "Hippocampal formation" - }, - { - "id": 554, - "tag": "CA1" - }, - { - "id": 544, - "tag": "Detailed cell model" - }, - { - "id": 614, - "tag": "CA1 Pyramidal cell" - }, - { - "id": 439, - "tag": "Hippocampus" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2023-12-21 18:21:03.875006+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/CA1PyramidalCell", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "294": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 294, - "name": "CATMAID Showcase", - "repository_type": "github", - "summary": "Project for example NeuroML files generated by [CATMAID](http://www.catmaid.org).\n", - "tags": [ - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 557, - "tag": "Connectomics" - }, - { - "id": 550, - "tag": "Neuronal reconstruction" - }, - { - "id": 132, - "tag": "Drosophila" - }, - { - "id": 615, - "tag": "Multiple" - } - ], - "timestamp_created": "2023-12-21 18:21:04.469476+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/CATMAIDShowcase", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "295": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 295, - "name": "OpenWorm C. elegans network model", - "repository_type": "github", - "summary": "![](http://www.opensourcebrain.org/attachments/download/22/medium.png)\r\n\r\n**The latest version of this model can be found at http://www.opensourcebrain.org/projects/c302**\r\nNote: the development of the OpenWorm model of [C. elegans](http://en.wikipedia.org/wiki/Caenorhabditis_elegans) is taking place at [http://www.openworm.org](http://www.openworm.org).\r\n\r\nA full list of those involved in that project can be found [here](http://www.openworm.org/people.html).\r\n\r\nThe C. elegans 3D model this was derived from was produced by Dr. Christian Grove and Dr. Paul Sternberg at the VirtualWorm project (WormBase, CalTech) and released into the public domain. You can visit the VirtualWorm home page at http://caltech.wormbase.org/virtualworm/ .\r\n\r\nFor details on running this neuroConstruct project see: https://github.com/openworm/OpenWorm/wiki/Running-the-C.-elegans-model-in-neuroConstruct.\r\n\r\nThis is a **work in progress**. Please [get in contact](http://www.openworm.org/contacts.html) for more information.\r\n", - "tags": [ - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 549, - "tag": "OpenWorm" - }, - { - "id": 558, - "tag": "Whole brain model" - }, - { - "id": 608, - "tag": "Blender" - }, - { - "id": 559, - "tag": "C. elegans" - }, - { - "id": 609, - "tag": "Nervous system" - }, - { - "id": 602, - "tag": "Network model" - } - ], - "timestamp_created": "2023-12-21 18:21:04.983784+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/openworm/CElegansNeuroML", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "296": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 296, - "name": "Cerebellar Nucleus Neuron - Steuber et al. 2011", - "repository_type": "github", - "summary": "![](http://www.opensourcebrain.org/attachments/download/34/medium.png)\r\n\r\n[Deep cerebellar nucleus neuron](http://neurolex.org/wiki/Category:Cerebellum_nucleus_reciprocal_projections_neuron) model from: Determinants of synaptic integration and heterogeneity in rebound firing explored with data-driven models of deep cerebellar nucleus cells. Steuber V, Schultheiss NW, Silver RA, De Schutter E, Jaeger D. J Comput Neurosci. 2011 Jun;30(3):633-58\r\n", - "tags": [ - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 544, - "tag": "Detailed cell model" - }, - { - "id": 546, - "tag": "Goldman-Hodgkin-Katz current" - }, - { - "id": 542, - "tag": "neuroConstruct" - }, - { - "id": 617, - "tag": "Cerebellar Nucleus Neuron" - }, - { - "id": 618, - "tag": "Cerebellum" - }, - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2023-12-21 18:21:05.512991+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/CerebellarNucleusNeuron", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "297": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 297, - "name": "Cerebellar Golgi Cell - Solinas et al. 2007", - "repository_type": "github", - "summary": "Multicompartmental model of cerebellar Golgi cell from: Solinas S, Forti L, Cesana E, Mapelli J, De Schutter E, D\u2019Angelo E. **Computational reconstruction of pacemaking and intrinsic electroresponsiveness in cerebellar Golgi cells**. [Front Cell Neurosci. 2007;1:2](http://journal.frontiersin.org/article/10.3389/neuro.03.002.2007/abstract). \r\n\r\nBased on implementation in NEURON taken from: http://senselab.med.yale.edu/modeldb/ShowModel.asp?model=112685.\r\n", - "tags": [ - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 544, - "tag": "Detailed cell model" - }, - { - "id": 542, - "tag": "neuroConstruct" - }, - { - "id": 618, - "tag": "Cerebellum" - }, - { - "id": 619, - "tag": "Golgi cell" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2023-12-21 18:21:06.009875+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/SolinasEtAl-GolgiCell", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "298": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 298, - "name": "ChannelWorm", - "repository_type": "github", - "summary": "An OpenWorm repository to integrate data, information, scripts, and models of ion channels in C. elegans", - "tags": [ - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 559, - "tag": "C. elegans" - }, - { - "id": 549, - "tag": "OpenWorm" - }, - { - "id": 560, - "tag": "Ion channels" - } - ], - "timestamp_created": "2023-12-21 18:21:06.513872+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/openworm/ChannelWorm", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "299": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 299, - "name": "Computational Neuroscience Ontology Showcase", - "repository_type": "github", - "summary": "![](http://www.opensourcebrain.org/attachments/download/103/CNO_image.jpg)\r\nScripts for interacting with the Computational Neuroscience Ontology:\r\nhttp://www.incf.org/programs/modeling/cno\r\n\r\nSee the [[Wiki]] for more details.\r\n", - "tags": [ - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 561, - "tag": "Ontology" - }, - { - "id": 562, - "tag": "Model annotation" - }, - { - "id": 563, - "tag": "Metadata" - }, - { - "id": 620, - "tag": "Python" - } - ], - "timestamp_created": "2023-12-21 18:21:07.000204+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/CNOShowcase", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "306": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 306, - "name": "Connection Set Algebra Showcase", - "repository_type": "github", - "summary": "![](http://www.opensourcebrain.org/projects/csashowcase/repository/revisions/master/entry/images/gaussian_sm.png)\n\nA project highlighting some of the features of the [Connection Set Algebra](http://software.incf.org/software/csa) library, and how it can interact with NeuroML & PyNN.\n\nFor more details see the [[Wiki]].\n", - "tags": [ - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 597, - "tag": "Python tools for computational neuroscience" - }, - { - "id": 620, - "tag": "Python" - } - ], - "timestamp_created": "2024-01-02 12:02:30.697835+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/CSAShowcase", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "307": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 307, - "name": "Dentate Gyrus - Santhakumar et al 2005", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 553, - "tag": "Hippocampal formation" - }, - { - "id": 623, - "tag": "Dentate gyrus" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 602, - "tag": "Network model" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2024-01-02 12:02:31.586706+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/DentateGyrus2005", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "308": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 308, - "name": "Self Sustained Network Activity - Destexhe 2009", - "repository_type": "github", - "summary": "Network simulations of self-sustained activity in networks of adaptive exponential integrate and fire neurons.\r\n\r\nFrom: Self-sustained asynchronous irregular states and Up\u2013Down states in thalamic, cortical and thalamocortical networks of nonlinear integrate-and-fire neurons, Alain Destexhe, [J Comp Neuroscience 2009](http://link.springer.com/article/10.1007%2Fs10827-009-0164-4)\r\n", - "tags": [ - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 551, - "tag": "Point neuron network" - }, - { - "id": 598, - "tag": "Adaptive exponential integrate and fire neuron" - }, - { - "id": 610, - "tag": "Generic" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 602, - "tag": "Network model" - } - ], - "timestamp_created": "2024-01-02 12:02:32.274908+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/destexhe_jcns_2009", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "309": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 309, - "name": "CovidWebProject", - "repository_type": "github", - "summary": "", - "tags": [], - "timestamp_created": "2024-01-04 09:40:26.895316+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/D-GopalKrishna/CovidWebProject", - "user": { - "email": "gopal@metacell.us", - "first_name": "D. Gopal ", - "id": "0db2937f-6534-434f-9e38-ff6ed1cbe395", - "last_name": "Krishna", - "username": "gopal" - }, - "user_id": "0db2937f-6534-434f-9e38-ff6ed1cbe395" - }, - "310": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 310, - "name": "dLGN Interneuron - Halnes et al 2011", - "repository_type": "github", - "summary": "A multi-compartment model for interneurons in the dLGN", - "tags": [ - { - "id": 624, - "tag": "Dorsal geniculate interneuron" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 625, - "tag": "Thalamus" - } - ], - "timestamp_created": "2024-01-04 15:04:50.257934+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/dLGNinterneuronHalnesEtAl2011", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "311": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 311, - "name": "Drosophila 3rd instar larval aCC motoneuron - Gunay et al 2014", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 544, - "tag": "Detailed cell model" - }, - { - "id": 132, - "tag": "Drosophila" - }, - { - "id": 626, - "tag": "Motoneuron" - }, - { - "id": 627, - "tag": "Neuromuscular system" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 628, - "tag": "XPP; NEURON" - }, - { - "id": 629, - "tag": "aCC motoneuron" - } - ], - "timestamp_created": "2024-01-04 15:33:48.760501+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/cengique/drosophila-aCC-L3-motoneuron-model", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "312": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 312, - "name": "Drosophila Projection Neuron - Gouwens and Wilson, 2009", - "repository_type": "github", - "summary": "![](/attachments/download/50/Selection_140.png)\r\n\r\nIn early stages of development!\r\n\r\nCell model based on pubmed:19439602. \r\n", - "tags": [ - { - "id": 630, - "tag": "Antennal lobe" - }, - { - "id": 544, - "tag": "Detailed cell model" - }, - { - "id": 132, - "tag": "Drosophila" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 631, - "tag": "Projection neuron" - }, - { - "id": 542, - "tag": "neuroConstruct" - } - ], - "timestamp_created": "2024-01-04 15:34:28.427023+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/Drosophila_Projection_Neuron", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "313": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 313, - "name": "Ebner et al. 2019 - Unified synaptic plasticity model", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 632, - "tag": "Synapse model" - } - ], - "timestamp_created": "2024-01-04 15:35:20.347593+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/EbnerEtAl2019", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "314": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 314, - "name": "NMDA spikes in L5 Pyramidal cells - Farinella et al 2014", - "repository_type": "github", - "summary": "Model from: **Glutamate-bound NMDARs arising from in vivo-like network activity extend spatio-temporal integration in a L5 cortical pyramidal cell model**\r\nMatteo Farinella, Daniel T. Ruedt, Padraig Gleeson, Frederic Lanore and\r\nR. Angus Silver\r\n\r\nThis paper has just [been published](http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003590).\r\n\r\nThe cell model used is based on: [Synaptic integration in L5 Pyramidal cell, Larkum et al. 2009](http://www.opensourcebrain.org/projects/larkumetal2009)\r\n", - "tags": [ - { - "id": 544, - "tag": "Detailed cell model" - }, - { - "id": 606, - "tag": "L5 pyramidal cell" - }, - { - "id": 633, - "tag": "NMDAR synapse" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 541, - "tag": "Pyramidal cell" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 542, - "tag": "neuroConstruct" - } - ], - "timestamp_created": "2024-01-04 15:36:17.392018+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/FarinellaEtAl_NMDAspikes", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "315": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 315, - "name": "VAnet2-GENESIS", - "repository_type": "github", - "summary": "This is an improved and much more efficient GENESIS implementation of\nthe dual exponential conductance version of the Vogels-Abbott (2005)\nnetwork model with Hodgkin-Huxley neurons and conductance-based\nsynaptic activation (COBA). Details are given in Brette et al. (2007).\nIt uses hsolve and runs 16 times faster than the original. The\n\u2018VAnet2-batch.g\u2019 script is intended to be extended for testing GENESIS\nspike timing dependent plasticity (STDP) implementations with hsolve.\n", - "tags": [ - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 610, - "tag": "Generic" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 602, - "tag": "Network model" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 634, - "tag": "Single compartment conductance based neuron model" - }, - { - "id": 635, - "tag": "Synaptic plasticity" - } - ], - "timestamp_created": "2024-01-04 16:19:11.249602+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/dbeeman/VAnet2-GENESIS", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "316": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 316, - "name": "FitzHugh Nagumo - FitzHugh 1969", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 636, - "tag": "Abstract neuron model" - }, - { - "id": 610, - "tag": "Generic" - }, - { - "id": 540, - "tag": "OSBv1" - } - ], - "timestamp_created": "2024-01-04 16:19:48.948658+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/FitzHugh-Nagumo", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "317": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 317, - "name": "FPGA Based Simulations Showcase", - "repository_type": "github", - "summary": "Information on various packages available for executing neuronal simulations on FPGA boards.\r\n\r\nFor more information, see the [[Wiki]].\r\n", - "tags": [ - { - "id": 637, - "tag": "FPGA" - }, - { - "id": 638, - "tag": "Hardware based simulation" - }, - { - "id": 540, - "tag": "OSBv1" - } - ], - "timestamp_created": "2024-01-04 16:19:49.466920+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/FPGAShowcase", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "318": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 318, - "name": "Functional Balanced Network", - "repository_type": "github", - "summary": "Orientation selectivity in inhibition-dominated networks of spiking neurons", - "tags": [ - { - "id": 611, - "tag": "NEST" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 604, - "tag": "Network" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 639, - "tag": "Visual system" - } - ], - "timestamp_created": "2024-01-04 16:19:49.970635+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/sdrsd/SadehRotter_2015_PLOS_ComputBiol", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "319": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 319, - "name": "Geppetto Showcase", - "repository_type": "github", - "summary": "![](/attachments/download/210/geppettologo.png)\r\n\r\nGeppetto is a web-based multi-algorithm, multi-scale simulation platform engineered to support the simulation of complex biological systems and their surrounding environment.\r\n\r\nExamples of NeuroML 2 files along with scripts for visualising/executing them in Geppetto (http://www.geppetto.org).\r\n", - "tags": [ - { - "id": 640, - "tag": "NeuroML" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 641, - "tag": "Simulator showcase" - } - ], - "timestamp_created": "2024-01-04 16:19:50.642934+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/openworm/org.geppetto.samples", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "320": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 320, - "name": "GHK & Nernst examples", - "repository_type": "github", - "summary": "Examples of cell models with currents using the [GHK flux equation](http://en.wikipedia.org/wiki/GHK_flux_equation), or using\n [Nernst\u2019s Equation](http://en.wikipedia.org/wiki/Nernst_equation) for the reversal potential in NeuroML 2 and a number of other formats.\n\nSee the [[Wiki]] for more details.\n", - "tags": [ - { - "id": 546, - "tag": "Goldman-Hodgkin-Katz current" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 540, - "tag": "OSBv1" - } - ], - "timestamp_created": "2024-01-04 16:19:51.118545+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/ghk-nernst", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "321": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 321, - "name": "Golgi Cell Dendritic Gap Junctions - Szoboszlay et al. 2016", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 618, - "tag": "Cerebellum" - }, - { - "id": 619, - "tag": "Golgi cell" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2024-01-04 16:19:51.586146+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/GolgiCellDendGapJunctions", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "322": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 322, - "name": "GPU Based Simulation Showcase", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 642, - "tag": "GPU" - }, - { - "id": 638, - "tag": "Hardware based simulation" - }, - { - "id": 540, - "tag": "OSBv1" - } - ], - "timestamp_created": "2024-01-04 16:19:52.079335+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/GPUShowcase", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "323": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 323, - "name": "Granule Cell Layer - Maex and De Schutter 1998", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 618, - "tag": "Cerebellum" - }, - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 633, - "tag": "NMDAR synapse" - }, - { - "id": 602, - "tag": "Network model" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 634, - "tag": "Single compartment conductance based neuron model" - }, - { - "id": 542, - "tag": "neuroConstruct" - } - ], - "timestamp_created": "2024-01-04 16:19:52.540241+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/GranCellLayer", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "324": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 324, - "name": "I&F granule cell model - Rothman & Piasini", - "repository_type": "github", - "summary": "This project contains an integrate and fire model of the cerebellar granule cell and a simple model of the mossy fibre to granule cell synapse. The cell model (IaF\\_GrC.nml) is the average (ie the one whose parameters have the average value) of the model population developed by Jason Rothman and published in Schwartz et, J Neurosci (2012). The synaptic model is based on the one used in that same paper, but it has been developed further to improve the fit to the experimental data and to ensure LEMS/NeuroMLv2 compatibility.\n", - "tags": [ - { - "id": 618, - "tag": "Cerebellum" - }, - { - "id": 643, - "tag": "Granule cell" - }, - { - "id": 644, - "tag": "Igor Pro" - }, - { - "id": 552, - "tag": "Integrate and fire neuron" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 542, - "tag": "neuroConstruct" - } - ], - "timestamp_created": "2024-01-04 16:19:53.146145+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/GranCellRothmanIf", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "325": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 325, - "name": "CA1 PV+ fast firing cell - Ferguson et al. 2013", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 645, - "tag": "Brian" - }, - { - "id": 646, - "tag": "CA1 PV fast-firing cell" - }, - { - "id": 553, - "tag": "Hippocampal formation" - }, - { - "id": 439, - "tag": "Hippocampus" - }, - { - "id": 555, - "tag": "Interneuron" - }, - { - "id": 556, - "tag": "Izhikevich neuron model" - }, - { - "id": 79, - "tag": "Mouse" - }, - { - "id": 540, - "tag": "OSBv1" - } - ], - "timestamp_created": "2024-01-04 16:26:02.274202+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/FergusonEtAl2013-PVFastFiringCell", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "326": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 326, - "name": "CovidWebProject", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 6, - "tag": "excitability" - }, - { - "id": 8, - "tag": "cortex" - } - ], - "timestamp_created": "2024-01-05 10:48:48.555278+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/D-GopalKrishna/CovidWebProject", - "user": { - "email": "nikhil0223@gmail.com", - "first_name": "Nikhil", - "id": "1c4ae9e0-7f65-4fb2-8254-2fc98bff0784", - "last_name": "Krishna", - "username": "nik" - }, - "user_id": "1c4ae9e0-7f65-4fb2-8254-2fc98bff0784" - }, - "327": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 327, - "name": "Cerebellar Granule Cell - Solinas et al. 2010", - "repository_type": "github", - "summary": "![](http://www.opensourcebrain.org/attachments/download/24/solinasal10.png)\r\nInitial version of Granule cell from: Solinas S., Nieus T, d\u2019Angelo E. (2010) **A Realistic Large-Scale Model of the Cerebellum Granular Layer Predicts Circuit Spatio-Temporal Filtering Properties**. Front Cell Neurosci. 2010;4:12.\r\n\r\nFor more information, see the [[Wiki]]\r\n\r\n[![Build Status](https://travis-ci.com/OpenSourceBrain/GranCellSolinasEtAl10.svg?branch=master)](https://travis-ci.com/OpenSourceBrain/GranCellSolinasEtAl10)\r\n", - "tags": [ - { - "id": 618, - "tag": "Cerebellum" - }, - { - "id": 643, - "tag": "Granule cell" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 633, - "tag": "NMDAR synapse" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 634, - "tag": "Single compartment conductance based neuron model" - }, - { - "id": 542, - "tag": "neuroConstruct" - } - ], - "timestamp_created": "2024-01-05 11:27:45.597115+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/GranCellSolinasEtAl10", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "328": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 328, - "name": "Granular Layer Network Model - Solinas, Nieus & D'Angelo 2010", - "repository_type": "github", - "summary": "Cerebellar granular layer network model from: Solinas S., Nieus T, d'Angelo E. (2010) [A Realistic Large-Scale Model of the Cerebellum Granular Layer Predicts Circuit Spatio-Temporal Filtering Properties](http://journal.frontiersin.org/article/10.3389/fncel.2010.00012/abstract). Front Cell Neurosci. 2010;4:12.", - "tags": [ - { - "id": 618, - "tag": "Cerebellum" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 602, - "tag": "Network model" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2024-01-05 11:27:46.682688+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/GranularLayerSolinasNieusDAngelo2010", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "329": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 329, - "name": "Granule Cell Layer - Piasini et al. ", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 648, - "tag": "AMPAR synapse" - }, - { - "id": 618, - "tag": "Cerebellum" - }, - { - "id": 552, - "tag": "Integrate and fire neuron" - }, - { - "id": 633, - "tag": "NMDAR synapse" - }, - { - "id": 602, - "tag": "Network model" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 551, - "tag": "Point neuron network" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 635, - "tag": "Synaptic plasticity" - }, - { - "id": 542, - "tag": "neuroConstruct" - } - ], - "timestamp_created": "2024-01-05 11:27:47.278917+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/epiasini/BillingsEtAl2014_GCL_Models", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "330": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 330, - "name": "Cerebellar Granule Cell - Maex De Schutter 1998", - "repository_type": "github", - "summary": "![](http://www.opensourcebrain.org/attachments/download/25/shutter98.png)\r\nA project illustrating the behaviour of the cerebellar granule cell model from: Maex, R and De Schutter, E. [Synchronization of Golgi and Granule Cell Firing in a Detailed Network Model of the Cerebellar Granule Cell Layer](http://www.ncbi.nlm.nih.gov/pubmed/9819260) J Neurophysiol, Nov 1998; 80: 2521 - 2537. \r\n\r\nBased on scripts obtained from: http://www.tnb.ua.ac.be/models/network.shtml.\r\n\r\nFor more details see the [[Wiki]].\r\n\r\n[![Build Status](https://travis-ci.org/OpenSourceBrain/GranuleCell.svg?branch=master)](https://travis-ci.org/OpenSourceBrain/GranuleCell)\r\n", - "tags": [ - { - "id": 618, - "tag": "Cerebellum" - }, - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 643, - "tag": "Granule cell" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 634, - "tag": "Single compartment conductance based neuron model" - }, - { - "id": 542, - "tag": "neuroConstruct" - } - ], - "timestamp_created": "2024-01-05 11:27:47.916357+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/GranuleCell", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "331": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 331, - "name": "Cerebellar Granule Cell - Maex De Schutter 1998", - "repository_type": "github", - "summary": "![](http://www.opensourcebrain.org/attachments/download/25/shutter98.png)\r\nA project illustrating the behaviour of the cerebellar granule cell model from: Maex, R and De Schutter, E. [Synchronization of Golgi and Granule Cell Firing in a Detailed Network Model of the Cerebellar Granule Cell Layer](http://www.ncbi.nlm.nih.gov/pubmed/9819260) J Neurophysiol, Nov 1998; 80: 2521 - 2537. \r\n\r\nBased on scripts obtained from: http://www.tnb.ua.ac.be/models/network.shtml.\r\n\r\nFor more details see the [[Wiki]].\r\n\r\n[![Build Status](https://travis-ci.org/OpenSourceBrain/GranuleCell.svg?branch=master)](https://travis-ci.org/OpenSourceBrain/GranuleCell)\r\n", - "tags": [ - { - "id": 618, - "tag": "Cerebellum" - }, - { - "id": 643, - "tag": "Granule cell" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 634, - "tag": "Single compartment conductance based neuron model" - }, - { - "id": 542, - "tag": "neuroConstruct" - }, - { - "id": 600, - "tag": "GENESIS" - } - ], - "timestamp_created": "2024-01-05 11:27:49.591429+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/GranuleCell", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "332": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 332, - "name": "Granule Cell Layer - CaycoGajicClopathSilver2017", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 618, - "tag": "Cerebellum" - }, - { - "id": 602, - "tag": "Network model" - }, - { - "id": 612, - "tag": "NeuroML2" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2024-01-05 11:27:50.222047+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/SilverLabUCL/MF-GC-network-backprop-public", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "333": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 333, - "name": "Hindmarsh and Rose 1984", - "repository_type": "github", - "summary": "The Hindmarsh Rose model consists of a fast spiking subsystem, which is a generalization of the Fitzhugh-Nagumo (aka Bonhoeffer-van der Pol) oscillator, coupled to a slower subsystem which allows the system to fire bursts of spikes.\n\nImplemented according to:\nHindmarsh J. L., and Rose R. M. (1984) A model of neuronal bursting using three coupled first order differential equations. Proc. R. Soc. London, Ser. B 221:87\u2013102.\n", - "tags": [ - { - "id": 636, - "tag": "Abstract neuron model" - }, - { - "id": 610, - "tag": "Generic" - }, - { - "id": 540, - "tag": "OSBv1" - } - ], - "timestamp_created": "2024-01-05 11:27:50.892195+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/HindmarshRose1984", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "334": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 334, - "name": "Hippocampus 3D Demo", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 649, - "tag": "Demo" - }, - { - "id": 553, - "tag": "Hippocampal formation" - }, - { - "id": 439, - "tag": "Hippocampus" - }, - { - "id": 604, - "tag": "Network" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 542, - "tag": "neuroConstruct" - } - ], - "timestamp_created": "2024-01-05 11:27:51.590687+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/Hippocampus3DDemo", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "335": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 335, - "name": "HNN", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 77, - "tag": "Human" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 604, - "tag": "Network" - }, - { - "id": 540, - "tag": "OSBv1" - } - ], - "timestamp_created": "2024-01-05 11:27:52.309406+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/HNN", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "336": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 336, - "name": "Hodgkin Huxley Tutorial", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 650, - "tag": "Giant axon" - }, - { - "id": 651, - "tag": "Hodgkin Huxley model" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 652, - "tag": "Squid" - }, - { - "id": 653, - "tag": "Tutorial" - } - ], - "timestamp_created": "2024-01-05 11:27:52.891655+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/openworm/hodgkin_huxley_tutorial", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "337": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 337, - "name": "Granule Cell - Steuber, Saviane & Berends", - "repository_type": "github", - "summary": "![](http://www.opensourcebrain.org/attachments/download/26/steubersaviane.png)\nConversion to NeuroML of a model granule cell developed by Volker Steuber and Chiara Saviane (Silver lab).\n\nBased on a Granule cell model from Michiel Berends, Neural Comp 15, 2531-47 (2005) originally modified by Chiara Saviane and further modified by VS, July 2006.\n", - "tags": [ - { - "id": 618, - "tag": "Cerebellum" - }, - { - "id": 643, - "tag": "Granule cell" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 634, - "tag": "Single compartment conductance based neuron model" - }, - { - "id": 542, - "tag": "neuroConstruct" - } - ], - "timestamp_created": "2024-01-05 11:31:14.072709+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/GranuleCellVSCS", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "338": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 338, - "name": "Izhikevich Spiking Neuron Model", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 636, - "tag": "Abstract neuron model" - }, - { - "id": 610, - "tag": "Generic" - }, - { - "id": 654, - "tag": "Google Summer of Code" - }, - { - "id": 556, - "tag": "Izhikevich neuron model" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 615, - "tag": "Multiple" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 540, - "tag": "OSBv1" - } - ], - "timestamp_created": "2024-01-05 11:31:16.975965+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/IzhikevichModel", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "339": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 339, - "name": "Large-Scale Circuit Model of the Primate Cortex - Joglekar et al. 2018", - "repository_type": "github", - "summary": "# JoglekarEtAl18\r\n\r\nInter-areal Balanced Amplification Enhances Signal Propagation in a Large-Scale Circuit Model of the Primate Cortex\r\nMadhura R. Joglekar, Jorge F. Mejia\r\ns, Guangyu Robert Yang, Xiao-Jing Wang\r\n\r\nhttps://www.cell.com/neuron/fulltext/S0896-6273(18)30152-1", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 78, - "tag": "Macaque" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 656, - "tag": "network" - } - ], - "timestamp_created": "2024-01-05 11:31:17.573114+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/JoglekarEtAl18", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "340": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 340, - "name": "Dendritic spikes in L2/3 pyramidal cells - Smith et al. 2013", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 544, - "tag": "Detailed cell model" - }, - { - "id": 657, - "tag": "L2/3 pyramidal cell" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 541, - "tag": "Pyramidal cell" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2024-01-05 11:31:18.085107+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/SmithEtAl2013-L23DendriticSpikes", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "341": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 341, - "name": "L2/3 Pyramidal Cell Tutorial", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 544, - "tag": "Detailed cell model" - }, - { - "id": 657, - "tag": "L2/3 pyramidal cell" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 541, - "tag": "Pyramidal cell" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 653, - "tag": "Tutorial" - } - ], - "timestamp_created": "2024-01-05 11:31:18.809608+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/L23PyramidalCellTutorial", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "342": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 342, - "name": "Layer 5b Pyramidal cell - Hay et al. 2011", - "repository_type": "github", - "summary": "![](https://raw.github.com/OpenSourceBrain/L5bPyrCellHayEtAl2011/master/neuroConstruct/images/large.png)\r\nLayer 5b Pyramidal cell constrained by experimental data on perisomatic firing properties as well as dendritic activity during backpropagation of the action potential.\r\n\r\n\r\nFrom: **Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties**, Etay Hay, Sean Hill, Felix Sch\u00fcrmann, Henry Markram and Idan Segev, [PLoS Comp Biol 2011](http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002107)\r\n\r\n[![Build Status](https://travis-ci.org/OpenSourceBrain/L5bPyrCellHayEtAl2011.svg?branch=master)](https://travis-ci.org/OpenSourceBrain/L5bPyrCellHayEtAl2011)\r\n", - "tags": [ - { - "id": 544, - "tag": "Detailed cell model" - }, - { - "id": 606, - "tag": "L5 pyramidal cell" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 541, - "tag": "Pyramidal cell" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 542, - "tag": "neuroConstruct" - } - ], - "timestamp_created": "2024-01-05 11:31:19.417216+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/L5bPyrCellHayEtAl2011", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "343": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 343, - "name": "Synaptic integration in L5 Pyramidal cell - Larkum et al. 2009", - "repository_type": "github", - "summary": "![](http://www.opensourcebrain.org/attachments/download/172/L5Spike.png)\n\nConversion to neuroConstruct/NeuroML of Layer 5 Pyramidal cell model from:\n\nLarkum ME, Nevian T, Sandler M, Polsky A, Schiller J (2009) Synaptic\nintegration in tuft dendrites of layer 5 pyramidal neurons: a new\nunifying principle. Science 325:756-60\n", - "tags": [ - { - "id": 544, - "tag": "Detailed cell model" - }, - { - "id": 606, - "tag": "L5 pyramidal cell" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 541, - "tag": "Pyramidal cell" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 542, - "tag": "neuroConstruct" - } - ], - "timestamp_created": "2024-01-05 11:31:20.000951+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/LarkumEtAl2009", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "344": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 344, - "name": "M1 Network Model", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 658, - "tag": "Large scale network simulation" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 659, - "tag": "NetPyNE" - }, - { - "id": 604, - "tag": "Network" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2024-01-05 11:31:20.542386+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/M1NetworkModel", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "345": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 345, - "name": "L5 Pyramidal Cell - Mainen et al. 1995", - "repository_type": "github", - "summary": "![](http://www.opensourcebrain.org/attachments/download/27/mainen95.png)\r\n\r\nImplementation of the Mainen et al. pyramidal cell model from: Mainen ZF, Joerges J, Huguenard JR, Sejnowski TJ (1995) A model of spike initiation in neocortical pyramidal neurons. [Neuron 15:1427-39](http://www.ncbi.nlm.nih.gov/pubmed/8845165). This project is based on scripts obtained from: http://senselab.med.yale.edu/senselab/modeldb/ShowModel.asp?model=8210\r\n", - "tags": [ - { - "id": 544, - "tag": "Detailed cell model" - }, - { - "id": 606, - "tag": "L5 pyramidal cell" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 541, - "tag": "Pyramidal cell" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 542, - "tag": "neuroConstruct" - } - ], - "timestamp_created": "2024-01-05 11:31:21.044964+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/MainenEtAl_PyramidalCell", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "346": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 346, - "name": "Large scale laminar cortical network - Mejias et al. 2016", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 78, - "tag": "Macaque" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 660, - "tag": "Rate based model" - }, - { - "id": 661, - "tag": "Whole brain" - } - ], - "timestamp_created": "2024-01-05 11:31:21.555072+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/MejiasEtAl2016", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "347": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 347, - "name": "3D Olfactory Bulb - Migliore et al. 2014", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 602, - "tag": "Network model" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 662, - "tag": "Olfactory bulb" - }, - { - "id": 663, - "tag": "Olfactory system" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2024-01-05 11:31:22.089611+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/MiglioreEtAl14_OlfactoryBulb3D", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "348": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 348, - "name": "ModelDB Showcase", - "repository_type": "github", - "summary": "OSB Showcase project for investigating options for interactions between [ModelDB](http://senselab.med.yale.edu/modeldb/) and OSB", - "tags": [ - { - "id": 664, - "tag": "Database" - }, - { - "id": 665, - "tag": "Model sharing" - }, - { - "id": 666, - "tag": "Neuroinformatics" - }, - { - "id": 540, - "tag": "OSBv1" - } - ], - "timestamp_created": "2024-01-05 11:31:22.673105+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/ModelDBShowcase", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "349": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 349, - "name": "Morris Lecar Model", - "repository_type": "github", - "summary": "Definition in LEMS/NeuroML 2 of the Morris Lecar Model. Code originally contributed by Daniel Salles Chevitarese and Yiyin Zhou.\r\n\r\nFor more information & latest status, see the [[Wiki]]\r\n", - "tags": [ - { - "id": 636, - "tag": "Abstract neuron model" - }, - { - "id": 667, - "tag": "Barnacle" - }, - { - "id": 668, - "tag": "MLAB" - }, - { - "id": 669, - "tag": "Muscle cell" - }, - { - "id": 627, - "tag": "Neuromuscular system" - }, - { - "id": 540, - "tag": "OSBv1" - } - ], - "timestamp_created": "2024-01-05 11:31:23.218667+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/MorrisLecarModel", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "350": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 350, - "name": "MouseLight Showcase", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 664, - "tag": "Database" - }, - { - "id": 545, - "tag": "Large scale brain initiative" - }, - { - "id": 79, - "tag": "Mouse" - }, - { - "id": 615, - "tag": "Multiple" - }, - { - "id": 609, - "tag": "Nervous system" - }, - { - "id": 550, - "tag": "Neuronal reconstruction" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 670, - "tag": "SWC" - } - ], - "timestamp_created": "2024-01-05 11:31:23.734728+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/MouseLightShowcase", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "351": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 351, - "name": "Multicompartmental granule cell - Diwakar et al. 2009", - "repository_type": "github", - "summary": "Multicompartmental cerebellar granule cell model.\n\nBased on: Diwakar S, Magistretti J, Goldfarb M, Naldi G, D\\`Angelo E (2009) Axonal Na+ channels ensure fast spike activation and back-propagation in cerebellar granule cells J Neurophysiol 101(2):519-32\n", - "tags": [ - { - "id": 618, - "tag": "Cerebellum" - }, - { - "id": 544, - "tag": "Detailed cell model" - }, - { - "id": 643, - "tag": "Granule cell" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2024-01-05 11:31:24.240383+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/rizland/cereb_grc_mc", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "352": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 352, - "name": "MultiTest", - "repository_type": "github", - "summary": "Testing networks at multiple scales", - "tags": [ - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 604, - "tag": "Network" - }, - { - "id": 612, - "tag": "NeuroML2" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2024-01-05 11:31:24.851719+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/pgleeson/multi", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "353": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 353, - "name": "MultiscaleISN", - "repository_type": "github", - "summary": "Inhibition Stabilized Networks at multiple scales based on Sadeh et al. 2017 ", - "tags": [ - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 604, - "tag": "Network" - }, - { - "id": 612, - "tag": "NeuroML2" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2024-01-05 11:31:25.428262+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/MultiscaleISN", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "355": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 355, - "name": "Muscle cell model - Boyle & Cohen 2008", - "repository_type": "github", - "summary": "![](http://www.openworm.org/img/OpenWormLogo.png)\r\n\r\nThis project is being developed as part of the [OpenWorm](http://www.openworm.org) project. This is an attempt to convert the model of [Boyle & Cohen, 2008](http://www.sciencedirect.com/science/article/pii/S0303264708001408) into NeuroML format for use on NEURON and other simulators.\r\n\r\nSee [here](/projects/muscle_model/wiki) for more details.\r\n", - "tags": [ - { - "id": 559, - "tag": "C. elegans" - }, - { - "id": 673, - "tag": "Muscle cell model" - }, - { - "id": 627, - "tag": "Neuromuscular system" - }, - { - "id": 674, - "tag": "Neuromusculature system" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 549, - "tag": "OpenWorm" - }, - { - "id": 2935, - "tag": "Cplusplus" - } - ], - "timestamp_created": "2024-01-05 11:36:13.825810+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/openworm/muscle_model", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "356": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 356, - "name": "MUSIC Showcase", - "repository_type": "github", - "summary": "[MUlti-SImulation Coordinator, MUSIC, ](http://incf.org/core/programs/modeling/projects/music/music-multi-simulation-coordinator/) is a software that allows large scale neuron simulators to communicate during runtime.\n\nThis OSB Showcase project will highlight some of the examples in development to illustrate its functionality.\n", - "tags": [ - { - "id": 675, - "tag": "MUSIC" - }, - { - "id": 540, - "tag": "OSBv1" - } - ], - "timestamp_created": "2024-01-05 11:36:14.353283+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/MUSICShowcase", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "357": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 357, - "name": "CA1 Network Model - Bezaire et al 2016", - "repository_type": "github", - "summary": "CA1 Network Model - Bezaire et al 2016\r\n", - "tags": [ - { - "id": 554, - "tag": "CA1" - }, - { - "id": 544, - "tag": "Detailed cell model" - }, - { - "id": 654, - "tag": "Google Summer of Code" - }, - { - "id": 676, - "tag": "High Performance Computing" - }, - { - "id": 553, - "tag": "Hippocampal formation" - }, - { - "id": 439, - "tag": "Hippocampus" - }, - { - "id": 555, - "tag": "Interneuron" - }, - { - "id": 658, - "tag": "Large scale network simulation" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 604, - "tag": "Network" - }, - { - "id": 677, - "tag": "Network oscillations" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 541, - "tag": "Pyramidal cell" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2024-01-05 11:36:14.874939+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/mbezaire/ca1", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "358": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 358, - "name": "Nengo - NeuroML interoperability", - "repository_type": "github", - "summary": "![](http://nengo.ca/sites/nengo.ca/files/logo_ctn.png)\n\nProject to test scenarios for NeuroML & [Nengo](http://nengo.ca) interoperability\n", - "tags": [ - { - "id": 610, - "tag": "Generic" - }, - { - "id": 658, - "tag": "Large scale network simulation" - }, - { - "id": 678, - "tag": "Nengo" - }, - { - "id": 602, - "tag": "Network model" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 597, - "tag": "Python tools for computational neuroscience" - }, - { - "id": 641, - "tag": "Simulator showcase" - } - ], - "timestamp_created": "2024-01-05 11:46:41.324195+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/NengoNeuroML", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "359": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 359, - "name": "NEST Showcase", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 610, - "tag": "Generic" - }, - { - "id": 611, - "tag": "NEST" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 604, - "tag": "Network" - }, - { - "id": 540, - "tag": "OSBv1" - } - ], - "timestamp_created": "2024-01-05 11:46:42.033119+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/NESTShowcase", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "360": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 360, - "name": "neuroConstruct Showcase", - "repository_type": "github", - "summary": "![](/attachments/download/111/logoMain.png)\n\nExample projects illustrating the functionality of [neuroConstruct](http://www.neuroconstruct.org/)\n", - "tags": [ - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 542, - "tag": "neuroConstruct" - }, - { - "id": 679, - "tag": "neuroconstruct" - } - ], - "timestamp_created": "2024-01-05 11:46:43.094723+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/neuroConstructShowcase", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "361": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 361, - "name": "NeuroElectro & SciUnit Showcase", - "repository_type": "github", - "summary": "![](/images/neuroelectro_logo.png)\r\n\r\nA project highlighting some of the possible interactions between OSB and [NeuroElectro](http://neuroelectro.org/) and [SciUnit](https://github.com/rgerkin/sciunit).\r\n\r\nNeuroElectro is one of the key external resources OSB will interact with, see [Interactions with other Neuroinformatics resources](http://www.opensourcebrain.org/projects/neuroinformatics/wiki/Wiki)\r\n", - "tags": [ - { - "id": 664, - "tag": "Database" - }, - { - "id": 610, - "tag": "Generic" - }, - { - "id": 666, - "tag": "Neuroinformatics" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 597, - "tag": "Python tools for computational neuroscience" - } - ], - "timestamp_created": "2024-01-05 11:46:43.688038+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/NeuroElectroSciUnit", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "362": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 362, - "name": "NeuroML2 Showcase ", - "repository_type": "github", - "summary": "The standard examples for NeuroML 2 from the repository for the specification.", - "tags": [ - { - "id": 680, - "tag": "--" - }, - { - "id": 681, - "tag": "---" - }, - { - "id": 682, - "tag": "NeuroML 2" - }, - { - "id": 540, - "tag": "OSBv1" - } - ], - "timestamp_created": "2024-01-05 11:46:44.229274+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/NeuroML/NeuroML2", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "363": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 363, - "name": "NeuroMorpho.Org Showcase", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 664, - "tag": "Database" - }, - { - "id": 615, - "tag": "Multiple" - }, - { - "id": 666, - "tag": "Neuroinformatics" - }, - { - "id": 550, - "tag": "Neuronal reconstruction" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 670, - "tag": "SWC" - } - ], - "timestamp_created": "2024-01-05 11:46:45.120531+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/NeuroMorpho", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "364": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 364, - "name": "Action Selection in the Basal Ganglia - Guthrie et al, 2013", - "repository_type": "github", - "summary": "Reproduction of a model of action selection in the basal ganglia (Guthrie et al., 2013)", - "tags": [ - { - "id": 683, - "tag": "Basal ganglia" - }, - { - "id": 610, - "tag": "Generic" - }, - { - "id": 602, - "tag": "Network model" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 620, - "tag": "Python" - } - ], - "timestamp_created": "2024-01-05 11:46:45.780221+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/rougier/Neurosciences/tree/master/basal-ganglia/guthrie-et-al-2013", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "365": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 365, - "name": "NIF and NeuroLex Showcase", - "repository_type": "github", - "summary": "![](http://www.opensourcebrain.org/attachments/download/85/nifneurolex.png)\n\nA repository for information, scripts and configuration files which demonstrate the interactions possible between [NIF](https://www.neuinfo.org), [NeuroLex](http://neurolex.org/wiki/Main_Page) and OSB.\n\nSee the [[Wiki]] for more details.\n", - "tags": [ - { - "id": 664, - "tag": "Database" - }, - { - "id": 562, - "tag": "Model annotation" - }, - { - "id": 665, - "tag": "Model sharing" - }, - { - "id": 666, - "tag": "Neuroinformatics" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 561, - "tag": "Ontology" - } - ], - "timestamp_created": "2024-01-05 11:46:46.426370+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/NIFShowcase", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "366": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 366, - "name": "NineML Showcase", - "repository_type": "github", - "summary": "![](/attachments/download/53/image_mini.png)\n\nExamples of models in [NineML](http://software.incf.org/software/nineml) (and the related language [SpineML](http://bimpa.group.shef.ac.uk/SpineML/index.php/Home)) and conversion to/from NeuroML/LEMS.\n\nSee the [[Wiki]] for more details.\n", - "tags": [ - { - "id": 665, - "tag": "Model sharing" - }, - { - "id": 684, - "tag": "NineML" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 685, - "tag": "SpineML" - }, - { - "id": 686, - "tag": "Standardization" - }, - { - "id": 687, - "tag": "XML" - } - ], - "timestamp_created": "2024-01-05 11:46:47.062885+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/NineMLShowcase", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "367": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 367, - "name": "DG Basket Cell - Norenberg et al. 2010", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 688, - "tag": "Basket cell" - }, - { - "id": 623, - "tag": "Dentate gyrus" - }, - { - "id": 544, - "tag": "Detailed cell model" - }, - { - "id": 553, - "tag": "Hippocampal formation" - }, - { - "id": 555, - "tag": "Interneuron" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 689, - "tag": "Passive model" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2024-01-05 11:46:47.618866+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/NorenbergEtAl2010_DGBasketCell", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "368": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 368, - "name": "NWB Showcase", - "repository_type": "github", - "summary": "Showcasing the interaction with [Neurodata Without Borders (NWB)](https://www.nwb.org) format experimental data on OSB.", - "tags": [ - { - "id": 24, - "tag": "NWB" - }, - { - "id": 540, - "tag": "OSBv1" - } - ], - "timestamp_created": "2024-01-05 11:46:48.127376+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/NWBShowcase", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "369": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 369, - "name": "Olfactory Bulb", - "repository_type": "github", - "summary": "A detailed network model of part of the rat olfactory bulb comprising compartmental mitral, granule and PG cells developed by Aditya Gilra and Upinder S. Bhalla (manuscript in preparation, 16 Apr 2013). The cell morphologies and network connections are in NeuroML v1.8.\n", - "tags": [ - { - "id": 690, - "tag": "MOOSE" - }, - { - "id": 602, - "tag": "Network model" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 662, - "tag": "Olfactory bulb" - }, - { - "id": 663, - "tag": "Olfactory system" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2024-01-05 11:46:48.722956+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/adityagilra/olfactory-bulb", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "370": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 370, - "name": "Olfactory Bulb Network Model - O'Connor, Angelo and Jacob 2012", - "repository_type": "github", - "summary": "![](http://www.opensourcebrain.org/attachments/download/92/Mitral_Cell_Model.png)\r\nA model of olfactory bulb mitral cells connected by apical dendrite gap junctions in a glomerular network\r\n", - "tags": [ - { - "id": 691, - "tag": "Mitral cell network" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 662, - "tag": "Olfactory bulb" - }, - { - "id": 663, - "tag": "Olfactory system" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 542, - "tag": "neuroConstruct" - } - ], - "timestamp_created": "2024-01-05 11:46:49.326461+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/Simon-at-Ely/OlfactoryBulbMitralCell", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "371": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 371, - "name": "Reduced CA3 model - Pinsky and Rinzel 1994", - "repository_type": "github", - "summary": "Reduced CA3 cell model from [Pinsky & Rinzel, Intrinsic and network rhythmogenesis in a reduced Traub model for CA3 neurons, Journal of Computational Neuroscience, 1994, Volume 1, Issue 1-2, pp 39-60](http://dx.doi.org/10.1007/BF00962717).\r\n\r\nFor more details, see the [Wiki](http://www.opensourcebrain.org/projects/pinskyrinzelmodel/wiki).\r\n\r\n \r\n", - "tags": [ - { - "id": 692, - "tag": "CA3" - }, - { - "id": 693, - "tag": "CA3 Pyramidal cell" - }, - { - "id": 694, - "tag": "Fortran" - }, - { - "id": 654, - "tag": "Google Summer of Code" - }, - { - "id": 553, - "tag": "Hippocampal formation" - }, - { - "id": 439, - "tag": "Hippocampus" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 695, - "tag": "Two compartment model" - } - ], - "timestamp_created": "2024-01-05 11:46:50.275326+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/PinskyRinzelModel", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "372": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 372, - "name": "Piriform Cortex Pyramidal Neuron", - "repository_type": "github", - "summary": "A conversion to NeuroML of the Piriform Cortex pyramidal cell model developed by Mike Vanier on GENESIS, see [here](http://users.cms.caltech.edu/~mvanier/science/parampaper/param.html).\r\n", - "tags": [ - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 663, - "tag": "Olfactory system" - }, - { - "id": 696, - "tag": "Piriform cortex" - }, - { - "id": 541, - "tag": "Pyramidal cell" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 542, - "tag": "neuroConstruct" - } - ], - "timestamp_created": "2024-01-05 11:46:50.921355+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/Simon-at-Ely/PiriformCortexPyramidalNeuron", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "373": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 373, - "name": "Minimal HH models - Pospischil et al. 2008", - "repository_type": "github", - "summary": "Conversion to NeuroML of cell models from: [Minimal Hodgkin\u2013Huxley type models for different classes of cortical and thalamic neurons](http://link.springer.com/article/10.1007/s00422-008-0263-8), Martin Pospischil, Maria Toledo-Rodriguez, Cyril Monier, Zuzanna Piwkowska, Thierry Bal, Yves Fr\u00e9gnac, Henry Markram and Alain Destexhe, Biological Cybernetics, 2008.\r\n", - "tags": [ - { - "id": 654, - "tag": "Google Summer of Code" - }, - { - "id": 615, - "tag": "Multiple" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 541, - "tag": "Pyramidal cell" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 634, - "tag": "Single compartment conductance based neuron model" - }, - { - "id": 542, - "tag": "neuroConstruct" - } - ], - "timestamp_created": "2024-01-05 11:46:51.604404+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/PospischilEtAl2008", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "374": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 374, - "name": "Spiking cortical network model - Potjans and Diesmann 2014", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 610, - "tag": "Generic" - }, - { - "id": 552, - "tag": "Integrate and fire neuron" - }, - { - "id": 658, - "tag": "Large scale network simulation" - }, - { - "id": 697, - "tag": "NEST SLI" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 602, - "tag": "Network model" - }, - { - "id": 540, - "tag": "OSBv1" - 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M. (1994). Based on Arnd Roth el al\u2019s conversion of the original GENESIS code to NEURON. Note: conversion not fully complete.\n", - "tags": [ - { - "id": 618, - "tag": "Cerebellum" - }, - { - "id": 544, - "tag": "Detailed cell model" - }, - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 698, - "tag": "Purkinje cell" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 542, - "tag": "neuroConstruct" - } - ], - "timestamp_created": "2024-01-05 11:46:52.738407+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/PurkinjeCell", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "376": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 376, - "name": "Pyloric pacemaker network - Prinz et al. 2003/04", - "repository_type": "github", - "summary": "Implementation of the pyloric network of the lobster stomatogastric ganglion system of Prinz, Marder, et al.\r\n\r\nWork in progress! For more details see the [[Wiki]].\r\n", - "tags": [ - { - "id": 699, - "tag": "C" - }, - { - "id": 700, - "tag": "Lobster" - }, - { - "id": 701, - "tag": "Multiple cells" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 551, - "tag": "Point neuron network" - }, - { - "id": 702, - "tag": "Stomatogastric ganglion" - }, - { - "id": 542, - "tag": "neuroConstruct" - } - ], - "timestamp_created": "2024-01-05 11:46:53.285117+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/PyloricNetwork", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "377": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 377, - "name": "PyNN Showcase", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 703, - "tag": "PyNN/NeuroML2" - } - ], - "timestamp_created": "2024-01-05 11:46:53.856510+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/PyNNShowcase", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "378": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 378, - "name": "L5 Pyramidal cell - Rothman et al. 2009", - "repository_type": "github", - "summary": "![](http://www.opensourcebrain.org/attachments/download/29/rothman2009.png)\nA project which was used in Rothman et al. \u201cSynaptic depression enables neuronal gain control\u201d Nature 2009 to demonstrate gain control in realistic cell models. Based on cell model from Kole et al. 2008 (obtained from http://senselab.med.yale.edu/modeldb/ShowModel.asp?model=114394).\n", - "tags": [ - { - "id": 544, - "tag": "Detailed cell model" - }, - { - "id": 606, - "tag": "L5 pyramidal cell" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 541, - "tag": "Pyramidal cell" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 542, - "tag": "neuroConstruct" - } - ], - "timestamp_created": "2024-01-05 11:46:54.347792+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/RothmanEtAl_KoleEtAl_PyrCell", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "379": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 379, - "name": "SadehEtAl2017-InhibitionStabilizedNetworks", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 610, - "tag": "Generic" - }, - { - "id": 611, - "tag": "NEST" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 604, - "tag": "Network" - }, - { - "id": 540, - "tag": "OSBv1" - } - ], - "timestamp_created": "2024-01-05 11:46:54.854132+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/SadehEtAl2017-InhibitionStabilizedNetworks", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "380": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 380, - "name": "SBML Showcase", - "repository_type": "github", - "summary": "![](/attachments/download/55/Official-sbml-supported-70.jpg)\r\n\r\nOSB showcase of interactions between SBML and NeuroML/LEMS.\r\n\r\nSee the [[Wiki]] for more details.\r\n", - "tags": [ - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 704, - "tag": "SBML" - }, - { - "id": 686, - "tag": "Standardization" - }, - { - "id": 705, - "tag": "Systems biology" - } - ], - "timestamp_created": "2024-01-05 11:46:55.373233+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/SBMLShowcase", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "381": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 381, - "name": "SpineShowcase", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 612, - "tag": "NeuroML2" - }, - { - "id": 540, - "tag": "OSBv1" - } - ], - "timestamp_created": "2024-01-05 11:46:55.955175+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/SpineShowcase", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "382": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 382, - "name": "Striatal Spiny Projection Neuron - Blackwell", - "repository_type": "github", - "summary": "[Striatal spiny projection neuron](http://neurolex.org/wiki/Category:Neostriatum_direct_pathway_spiny_neuron) model being developed by Avrama Blackwell. A version of this model in \u201cpure\u201d NeuroML v2.0 is being developed here.\n\nFor more information, see the [[Wiki]]\n", - "tags": [ - { - "id": 683, - "tag": "Basal ganglia" - }, - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 546, - "tag": "Goldman-Hodgkin-Katz current" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 634, - "tag": "Single compartment conductance based neuron model" - }, - { - "id": 706, - "tag": "Striatal Spiny Projection Neuron" - } - ], - "timestamp_created": "2024-01-05 11:46:56.459952+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/StriatalSpinyProjectionNeuron", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "383": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 383, - "name": "Synaptic Integration Demo", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 610, - "tag": "Generic" - }, - { - "id": 707, - "tag": "L23 Pyramidal cell" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 682, - "tag": "NeuroML 2" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 653, - "tag": "Tutorial" - } - ], - "timestamp_created": "2024-01-05 11:46:57.031305+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/SynapticIntegration", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "384": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 384, - "name": "TCrelay neuron in high conductance state - Zeldenrust et al. 2018", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 79, - "tag": "Mouse" - }, - { - "id": 708, - "tag": "Neuron" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 709, - "tag": "Thalamocortical relay cell" - }, - { - "id": 625, - "tag": "Thalamus" - } - ], - "timestamp_created": "2024-01-05 11:46:57.584685+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/fleurzeldenrust/TCrelay-Neuron-model", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "385": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 385, - "name": "Thalamocortical network - Traub et al. 2005", - "repository_type": "github", - "summary": "![](http://www.opensourcebrain.org/attachments/download/125/Column3_S.png)\r\nThis is a project implementing cells from the thalamocortical network model of Traub et al 2005 in NeuroML. Based on the NEURON implementation from: http://senselab.med.yale.edu/ModelDB/ShowModel.asp?model=45539.\r\n\r\nThe [[Wiki]] gives details of running this project with neuroConstruct.\r\n", - "tags": [ - { - "id": 544, - "tag": "Detailed cell model" - }, - { - "id": 710, - "tag": "FORTRAN" - }, - { - "id": 711, - "tag": "Gap junctions" - }, - { - "id": 676, - "tag": "High Performance Computing" - }, - { - "id": 555, - "tag": "Interneuron" - }, - { - "id": 658, - "tag": "Large scale network simulation" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 602, - "tag": "Network model" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 541, - "tag": "Pyramidal cell" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 542, - "tag": "neuroConstruct" - } - ], - "timestamp_created": "2024-01-05 11:46:58.127700+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/Thalamocortical", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "386": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 386, - "name": "The Virtual Brain Showcase", - "repository_type": "github", - "summary": "![](http://www.opensourcebrain.org/attachments/download/177/Logo_VB.png)\r\n\r\nRepository for an OSB project to show the interaction between neural mass models implemented in [The Virtual Brain](http://www.thevirtualbrain.org) and how they can make use of model based description languages like NeuroML\r\n", - "tags": [ - { - "id": 712, - "tag": "Neural population model" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 713, - "tag": "TheVirtualBrain" - }, - { - "id": 558, - "tag": "Whole brain model" - } - ], - "timestamp_created": "2024-01-05 11:46:58.692317+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/TheVirtualBrainShowcase", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "387": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 387, - "name": "Tobin et al. 2017", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 132, - "tag": "Drosophila" - }, - { - "id": 615, - "tag": "Multiple" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 542, - "tag": "neuroConstruct" - } - ], - "timestamp_created": "2024-01-05 11:46:59.301580+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/TobinEtAl2017", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "388": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 388, - "name": "Network models of V1", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 654, - "tag": "Google Summer of Code" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 602, - "tag": "Network model" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 551, - "tag": "Point neuron network" - }, - { - "id": 714, - "tag": "PyNN" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 639, - "tag": "Visual system" - } - ], - "timestamp_created": "2024-01-05 11:46:59.805421+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/V1NetworkModels", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "389": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 389, - "name": "VERTEX Showcase", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 602, - "tag": "Network model" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2024-01-05 11:47:00.382311+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/VERTEXShowcase", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "390": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 390, - "name": "Golgi Cell Network - Vervaeke et al 2010", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 618, - "tag": "Cerebellum" - }, - { - "id": 544, - "tag": "Detailed cell model" - }, - { - "id": 711, - "tag": "Gap junctions" - }, - { - "id": 555, - "tag": "Interneuron" - }, - { - "id": 602, - "tag": "Network model" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 603, - "tag": "Rodent" - }, - { - "id": 542, - "tag": "neuroConstruct" - } - ], - "timestamp_created": "2024-01-05 11:47:00.882649+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/VervaekeEtAl-GolgiCellNetwork", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "391": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 391, - "name": "Virtual Fly Brain Showcase", - "repository_type": "github", - "summary": "![](/attachments/download/46/flyBrain.gif)\r\n\r\nShowcase of some *Drosophila melanogaster* neuronal morphologies processed by [Virtual Fly Brain](http://www.virtualflybrain.org) team. These will likely include examples based on raw data available from http://flybrain.stanford.edu and http://flycircuit.tw.\r\n", - "tags": [ - { - "id": 132, - "tag": "Drosophila" - }, - { - "id": 615, - "tag": "Multiple" - }, - { - "id": 550, - "tag": "Neuronal reconstruction" - }, - { - "id": 540, - "tag": "OSBv1" - } - ], - "timestamp_created": "2024-01-05 11:47:01.408560+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/jefferis/osb_vfb_showcase", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "392": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 392, - "name": "VierlingClaassenEtAl2010", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 602, - "tag": "Network model" - }, - { - "id": 677, - "tag": "Network oscillations" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2024-01-05 11:47:02.011988+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/VierlingClaassenEtAl2010", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "393": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 393, - "name": "Balanced network with inhibitory plasticity - Vogels et al. 2011", - "repository_type": "github", - "summary": "![](/attachments/download/107/test_full.png)\n\nNetwork model from: Vogels TP, Sprekeler H, Zenke F, Clopath C, Gerstner W (2011) [Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks](http://www.sciencemag.org/content/334/6062/1569.abstract). Science 334:1569-73.\n\nSee the [[Wiki]] for more details.\n", - "tags": [ - { - "id": 715, - "tag": "C/Matlab" - }, - { - "id": 610, - "tag": "Generic" - }, - { - "id": 654, - "tag": "Google Summer of Code" - }, - { - "id": 716, - "tag": "Inhibition" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 602, - "tag": "Network model" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 551, - "tag": "Point neuron network" - } - ], - "timestamp_created": "2024-01-05 11:47:02.540017+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/VogelsEtAl2011", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "394": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 394, - "name": "Wang & Buzsaki 1996", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 717, - "tag": "?" - }, - { - "id": 553, - "tag": "Hippocampal formation" - }, - { - "id": 439, - "tag": "Hippocampus" - }, - { - "id": 555, - "tag": "Interneuron" - }, - { - "id": 604, - "tag": "Network" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2024-01-05 11:47:03.177075+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/WangBuzsaki1996", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "395": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 395, - "name": "Laminar organization of motor cortex - Weiler et al 2008", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 602, - "tag": "Network model" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 603, - "tag": "Rodent" - } - ], - "timestamp_created": "2024-01-05 11:47:03.666639+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/WeilerEtAl08-LaminarCortex", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "396": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 396, - "name": "Wilson and Cowan model", - "repository_type": "github", - "summary": "", - "tags": [ - { - "id": 610, - "tag": "Generic" - }, - { - "id": 601, - "tag": "Neocortex" - }, - { - "id": 604, - "tag": "Network" - }, - { - "id": 708, - "tag": "Neuron" - }, - { - "id": 540, - "tag": "OSBv1" - }, - { - "id": 660, - "tag": "Rate based model" - } - ], - "timestamp_created": "2024-01-05 11:47:04.150198+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/WilsonCowan", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "397": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 397, - "name": "Low Threshold Calcium Currents in TC cells (Destexhe et al 1998)", - "repository_type": "github", - "summary": "In Destexhe, Neubig, Ulrich, and Huguenard (1998) experiments and models examine low threshold calcium current's (IT, or T-current) distribution in thalamocortical (TC) cells. Multicompartmental modeling supports the hypothesis that IT currents have a density at least several fold higher in the dendrites than the soma. The IT current contributes significantly to rebound bursts and is thought to have important network behavior consequences. See the paper for details. See also http://cns.iaf.cnrs-gif.fr Correspondance may be addressed to Alain Destexhe: Destexhe@iaf.cnrs-gif.fr", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 573, - "tag": "Rebound firing" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 875, - "tag": "ModelDB:279" - } - ], - "timestamp_created": "2024-01-05 14:49:54.137757+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/279", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "398": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 398, - "name": "Olfactory Mitral Cell (Davison et al 2000)", - "repository_type": "github", - "summary": "A four-compartment model of a mammalian olfactory bulb mitral cell, reduced \r\nfrom the complex 286-compartment model described by Bhalla and Bower (1993). \r\nThe compartments are soma/axon, secondary dendrites, primary dendrite shaft \r\nand primary dendrite tuft. The reduced model runs 75 or more times faster \r\nthan the full model, making its use in large, realistic network models of the \r\nolfactory bulb practical.", - "tags": [ - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 876, - "tag": "ModelDB:2487" - } - ], - "timestamp_created": "2024-01-05 14:51:42.956889+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/2487", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "399": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 399, - "name": "Influence of dendritic structure on neocortical neuron firing patterns (Mainen and Sejnowski 1996)", - "repository_type": "github", - "summary": "This package contains compartmental models of four reconstructed neocortical neurons (layer 3 Aspiny, layer 4 Stellate, layer 3 and layer 5 Pyramidal neurons) with active dendritic currents using NEURON. Running this simulation demonstrates that an entire spectrum of firing patterns can be reproduced in this set of model neurons which share a common distribution of ion channels and differ only in their dendritic geometry. The reference paper is: Z. F. Mainen and T. J. Sejnowski (1996) Influence of dendritic structure on firing pattern in model neocortical neurons. Nature 382: 363-366. See also http://www.cnl.salk.edu/~zach/methods.html and http://www.cnl.salk.edu/~zach/ More info in readme.txt file below made visible by clicking on the patdemo folder and then on the readme.txt file.", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 877, - "tag": "ModelDB:2488" - } - ], - "timestamp_created": "2024-01-05 14:51:43.627369+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/2488", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "400": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 400, - "name": "Olfactory Bulb Network (Davison et al 2003)", - "repository_type": "github", - "summary": "A biologically-detailed model of the mammalian olfactory bulb, incorporating \r\nthe mitral and granule cells and the dendrodendritic synapses between them. \r\nThe results of simulation experiments with electrical stimulation agree \r\nclosely in most details with published experimental data. The model predicts \r\nthat the time course of dendrodendritic inhibition is dependent on the \r\nnetwork connectivity as well as on the intrinsic parameters of the synapses. \r\nIn response to simulated odor stimulation, strongly activated mitral cells \r\ntend to suppress neighboring cells, the mitral cells readily synchronize \r\ntheir firing, and increasing the stimulus intensity increases the degree of \r\nsynchronization. For more details, see the reference below.", - "tags": [ - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 878, - "tag": "ModelDB:2730" - } - ], - "timestamp_created": "2024-01-05 14:51:44.290571+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/2730", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "401": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 401, - "name": "Olfactory Mitral Cell (Bhalla, Bower 1993)", - "repository_type": "github", - "summary": "This is a conversion to NEURON of the mitral cell model described in Bhalla \r\nand Bower (1993). \r\nThe original model was written in GENESIS and is available by joining BABEL, the GENESIS users' group here http://www.genesis-sim.org/GENESIS/babel.html", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 879, - "tag": "ModelDB:2733" - } - ], - "timestamp_created": "2024-01-08 11:43:21.510572+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/2733", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "404": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 404, - "name": "CA1 Pyramidal Neuron: slow Na+ inactivation (Migliore 1996)", - "repository_type": "github", - "summary": "Model files from the paper: \r\nM. Migliore, Modeling the attenuation and failure of action potentials in \r\nthe dendrites of hippocampal neurons, Biophys. J. 71:2394-403 (1996). Please see the below readme file for installation and use instructions. Contact michele.migliore@pa.ibf.cnr.it\r\n if you have any questions about the implementation of the model.", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 882, - "tag": "ModelDB:2937" - } - ], - "timestamp_created": "2024-01-08 14:21:32.759287+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/2937", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "405": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 405, - "name": "Estimation and Production of Time Intervals (Migliore et al 2001)", - "repository_type": "github", - "summary": "NEURON model files from the paper \r\nM. Migliore, L. Messineo, M. Cardaci, G.F. Ayala, \r\nQuantitative modeling of perception and production of time intervals, J.Neurophysiol. 86, 2754-2760 (2001). Contact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model.", - "tags": [ - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 883, - "tag": "ModelDB:3167" - } - ], - "timestamp_created": "2024-01-08 14:21:33.371691+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3167", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "406": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 406, - "name": "CA3 Pyramidal Neuron (Migliore et al 1995)", - "repository_type": "github", - "summary": "Model files from the paper:\r\nM. Migliore, E. Cook, D.B. Jaffe, D.A. Turner and D. Johnston, Computer\r\nsimulations of morphologically reconstructed CA3 hippocampal neurons, J.\r\nNeurophysiol. 73, 1157-1168 (1995).\r\n\r\nDemonstrates how the same cell could be bursting or non bursting according to the Ca-independent conductance densities. Includes calculation of intracellular Calcium. Instructions are provided in the below README file. Contact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model.", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 884, - "tag": "ModelDB:3263" - } - ], - "timestamp_created": "2024-01-08 14:21:33.872700+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3263", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "407": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 407, - "name": "Short term plasticity of synapses onto V1 layer 2/3 pyramidal neuron (Varela et al 1997)", - "repository_type": "github", - "summary": "This archive contains 3 mod files for NEURON that implement the short term \r\nsynaptic plasticity model described in\r\n Varela, J.A., Sen, K., Gibson, J., Fost, J., Abbott, L.R., \r\n and Nelson, S.B.. \r\n A quantitative description of short-term plasticity at \r\n excitatory synapses in layer 2/3 of rat primary visual cortex.\r\n Journal of Neuroscience 17:7926-7940, 1997.\r\n\r\nContact ted.carnevale@yale.edu if you have questions \r\nabout this implementation of the model.", - "tags": [ - { - "id": 722, - "tag": "Depression" - }, - { - "id": 723, - "tag": "Facilitation" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 726, - "tag": "Vision" - }, - { - "id": 885, - "tag": "ModelDB:3264" - } - ], - "timestamp_created": "2024-01-08 14:21:34.498901+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3264", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "408": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 408, - "name": "Pyramidal Neuron Deep: K+ kinetics (Korngreen, Sakmann 2000)", - "repository_type": "github", - "summary": "NEURON mod files for the slow and fast K+ currents from the paper:\r\nVoltage-gated K+ channels in layer 5 neocortical pyramidal neurones from young rats: subtypes and gradients\r\nA. Korngreen and B. Sakmann, J.Physiol. 525.3, 621-639 (2000).", - "tags": [ - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 886, - "tag": "ModelDB:3289" - } - ], - "timestamp_created": "2024-01-08 14:21:34.962662+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3289", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "409": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 409, - "name": "CN Octopus Cell: Ih current (Bal, Oertel 2000)", - "repository_type": "github", - "summary": "NEURON mod files for the Ih current from the paper \r\nR. Bal and D. Oertel\r\nHyperpolarization-Activated, Mixed-Cation Current (Ih) in Octopus Cells of the Mammalian Cochlear Nucleus, J. Neurophysiol. 84, 806-817 (2000).\r\nContact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model.", - "tags": [ - { - "id": 594, - "tag": "I h" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 887, - "tag": "ModelDB:3332" - } - ], - "timestamp_created": "2024-01-08 14:21:35.574861+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3332", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "410": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 410, - "name": "Action potential initiation in the olfactory mitral cell (Shen et al 1999)", - "repository_type": "github", - "summary": "Mitral cell model with standard parameters for the paper:\r\nShen, G.Y., Chen, W. R., Midtgaard, J., Shepherd, G.M., and Hines, M.L.\r\n(1999)\r\nComputational Analysis of Action Potential Initiation in Mitral\r\nCell Soma and Dendrites Based on Dual Patch Recordings.\r\nJournal of Neurophysiology 82:3006. Contact Michael.Hines@yale.edu if you have any questions about the implementation of the model.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 888, - "tag": "ModelDB:3342" - } - ], - "timestamp_created": "2024-01-08 14:21:36.200340+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3342", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "411": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 411, - "name": "Thalamocortical and Thalamic Reticular Network (Destexhe et al 1996)", - "repository_type": "github", - "summary": "NEURON model of oscillations in networks of thalamocortical and thalamic reticular neurons in the ferret. (more applications for a model quantitatively identical to previous DLGN model; updated for NEURON v4 and above)", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 592, - "tag": "Sleep" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 593, - "tag": "Spindles" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 889, - "tag": "ModelDB:3343" - } - ], - "timestamp_created": "2024-01-08 14:21:36.841428+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3343", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "412": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 412, - "name": "DG granule cell: I-A model (Beck et al 1992)", - "repository_type": "github", - "summary": "NEURON mod files for the I-A current from the paper:\r\nBeck H, Ficker E, Heinemann U.\r\nProperties of two voltage-activated potassium currents in \r\nacutely isolated juvenile rat dentate gyrus granule cells.\r\nJ. Neurophysiol. 68, 2086-2099 (1992) Contact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model.", - "tags": [ - { - "id": 590, - "tag": "I A" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 890, - "tag": "ModelDB:3344" - } - ], - "timestamp_created": "2024-01-08 14:21:37.367176+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3344", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "413": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 413, - "name": "Coincidence detection in avian brainstem (Simon et al 1999)", - "repository_type": "github", - "summary": "A detailed biophysical model of coincidence\r\ndetector neurons in the nucleus laminaris (auditory brainstem) which are\r\npurported to detect interaural time differences (ITDs) from Simon et al 1999.", - "tags": [ - { - "id": 595, - "tag": "Coincidence Detection" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 891, - "tag": "ModelDB:3434" - } - ], - "timestamp_created": "2024-01-08 14:21:37.885916+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3434", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "414": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 414, - "name": "MNTB Neuron: Kv3.1 currents (Wang et al 1998)", - "repository_type": "github", - "summary": "Model of Medial Nucleus of the Trapezoid Body (MNTB) neurons described in Lu-Yang Wang, Li Gan, Ian D. Forsythe and Leonard K. Kaczmarek. Contribution of the Kv3.1 potassium channel to high-frequency firing in mouse auditory neurones. J. Physiol (1998) 509.1 183-194. Created by David Kornfeld, Byram Hills High School, Armonk NY. Please email dbk1@mindspring.com for questions about the model. See Readme.txt below for more info.", - "tags": [ - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 892, - "tag": "ModelDB:3454" - } - ], - "timestamp_created": "2024-01-08 14:21:38.382761+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3454", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "416": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 416, - "name": "Retinal Ganglion Cell: I-Na,t (Benison et al 2001)", - "repository_type": "github", - "summary": "NEURON mod files for the Na current from the papers:\r\n(model)\r\nBenison G, Keizer J, Chalupa LM, Robinson DW. Modeling temporal behavior of postnatal cat retinal ganglion cells. J Theor Biol. 2001 210:187-99 and a reference from this paper: (experimental)\r\nSkaliora I, Scobey RP, Chalupa LM. Prenatal development of excitability in cat retinal ganglion cells: action potentials and sodium currents. J Neurosci 1993 13:313-23. See the readme.txt file below for more information.", - "tags": [ - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 894, - "tag": "ModelDB:3483" - } - ], - "timestamp_created": "2024-01-08 14:21:39.373088+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3483", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "420": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 420, - "name": "CA1 pyramidal neuron (Migliore et al 1999)", - "repository_type": "github", - "summary": "Hippocampal CA1 pyramidal neuron model from the paper \r\nM.Migliore, D.A Hoffman, J.C. Magee and D. Johnston (1999) Role of an A-type K+ conductance in the back-propagation of action potentials in the dendrites of hippocampal pyramidal neurons,\r\nJ. Comput. Neurosci. 7, 5-15. Instructions are provided in the below README file.Contact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model.", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 880, - "tag": "ModelDB:2796" - } - ], - "timestamp_created": "2024-01-08 15:44:23.735331+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/2796", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "423": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 423, - "name": "Hopfield and Brody model (Hopfield, Brody 2000)", - "repository_type": "github", - "summary": "NEURON implementation of the Hopfield and Brody model from the papers:\r\nJJ Hopfield and CD Brody (2000)\r\nJJ Hopfield and CD Brody (2001). Instructions are provided in the below readme.txt file.", - "tags": [ - { - "id": 718, - "tag": "Attractor Neural Network" - }, - { - "id": 595, - "tag": "Coincidence Detection" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 719, - "tag": "Pattern Recognition" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 881, - "tag": "ModelDB:2798" - } - ], - "timestamp_created": "2024-01-09 10:59:28.168307+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/2798", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "424": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 424, - "name": "Retinal Ganglion Cell: I-A (Benison et al 2001)", - "repository_type": "github", - "summary": "NEURON mod files for the K-A current from the papers: (model) Benison G, Keizer J, Chalupa LM, Robinson DW. Modeling temporal behavior of postnatal cat retinal ganglion cells. J.Theor.Biol. 210:187-199 (2001) and (experiment) Skaliora I, Robinson DW, Scobey RP, Chalupa LM., Properties of K+ conductances in cat retinal ganglion cells during the period of activity-mediated refinements in retinofugal pathways. Eur.J.Neurosci. 7:1558-1568 (1995).", - "tags": [ - { - "id": 590, - "tag": "I A" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 895, - "tag": "ModelDB:3488" - } - ], - "timestamp_created": "2024-01-09 11:16:11.635795+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3488", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "425": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 425, - "name": "Retinal Ganglion Cell: I-K (Skaliora et al 1995)", - "repository_type": "github", - "summary": "NEURON mod files for the K-DR current from the paper: \r\nSkaliora I, Robinson DW, Scobey RP, Chalupa LM. Properties of K+ conductances in cat retinal ganglion cells during the period of activity-mediated refinements in retinofugal pathways.\r\nEur J Neurosci 1995 7(7):1558-1568. See the readme.txt file below for more information.", - "tags": [ - { - "id": 576, - "tag": "I K" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 896, - "tag": "ModelDB:3491" - } - ], - "timestamp_created": "2024-01-09 11:31:51.583775+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3491", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "426": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 426, - "name": "Spinal Motor Neuron: Na, K_A, and K_DR currents (Safronov, Vogel 1995)", - "repository_type": "github", - "summary": "NEURON mod files for the Na, K-A, and K-DR currents from the paper: \r\nSafronov, B.V. and Vogel,W. Single voltage-activated Na+ and K+ channels in the somata of rat motorneurons. Journal of Physiology 487.1:91-106 (1995). See the readme.txt file for more information.", - "tags": [ - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 897, - "tag": "ModelDB:3493" - } - ], - "timestamp_created": "2024-01-09 11:31:52.266145+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3493", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "427": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 427, - "name": "Xenopus Myelinated Neuron (Frankenhaeuser, Huxley 1964)", - "repository_type": "github", - "summary": "Frankenhaeuser, B. and Huxley, A. F. (1964),\r\nThe action potential in the myelinated nerve fiber of Xenopus Laevis as computed on the basis of voltage clamp data. J. Physiol. 171: 302-315. See README file for more information.", - "tags": [ - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 898, - "tag": "ModelDB:3507" - } - ], - "timestamp_created": "2024-01-09 11:31:52.856885+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3507", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "428": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 428, - "name": "Ca-dependent K Channel: kinetics from rat muscle (Moczydlowski, Latorre 1983) NEURON", - "repository_type": "github", - "summary": "Macroscopic channel model based on\r\nMoczydlowski, E. and Latorre, R. (1983).\r\nGating kinetics of Ca++ activated K+ channels from rat muscle incorporated into planar lipid bilayers. \r\nJ. Gen. Physiol. 82: 511-542 \r\nSee README file for more information.", - "tags": [ - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 899, - "tag": "ModelDB:3509" - } - ], - "timestamp_created": "2024-01-09 11:31:53.473463+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3509", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "429": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 429, - "name": "Regulation of a slow STG rhythm (Nadim et al 1998)", - "repository_type": "github", - "summary": "Frequency regulation of a slow rhythm by a fast periodic input. Nadim, F., Manor, Y., Nusbaum, M. P., Marder, E. (1998) J. Neurosci. 18: 5053-5067", - "tags": [ - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 900, - "tag": "ModelDB:3511" - } - ], - "timestamp_created": "2024-01-09 11:31:53.970778+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3511", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "430": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 430, - "name": "Thalamic Relay Neuron: I-T current (Williams, Stuart 2000)", - "repository_type": "github", - "summary": "NEURON mod files for the Ca-T current from the paper:\r\nWilliams SR, Stuart GJ, Action potential backpropagation and \r\nsomato-dendritic distribution of ion channels in thalamocortical neurons.\r\nJ Neurosci. 2000 20:1307-17.\r\nContact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model.", - "tags": [ - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 901, - "tag": "ModelDB:3533" - } - ], - "timestamp_created": "2024-01-09 11:31:54.595818+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3533", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "431": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 431, - "name": "Olfactory Mitral Cell: I-A and I-K currents (Wang et al 1996)", - "repository_type": "github", - "summary": "NEURON mod files for the I-A and I-K currents from the paper:\r\nX.Y. Wang, J.S. McKenzie and R.E. Kemm, Whole-cell K+ currents in identified olfactory \r\nbulb output neurones of rats. J Physiol. 1996 490.1:63-77. Please see the readme.txt included in the model file for more information.", - "tags": [ - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 902, - "tag": "ModelDB:3648" - } - ], - "timestamp_created": "2024-01-09 11:31:55.078439+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3648", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "432": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 432, - "name": "Glutamate diffusion and AMPA receptor activation in the cerebellar glomerulus (Saftenku 2005)", - "repository_type": "github", - "summary": "Synaptic conductances are influenced markedly by the geometry of the space surrounding the synapse since the transient glutamate concentration in the synaptic cleft is determined by this geometry. Our paper is an attempt to understand the reasons for slow glutamate diffusion in the cerebellar glomerulus, a structure situated around the enlarged mossy fiber terminal in the cerebellum and surrounded by a glial sheath. \r\n...\r\nOur results suggest at least a 7- to 10-fold lower apparent diffusion coefficient of glutamate in the porous medium of the glomerulus than in water. \r\n... See paper for details and more.", - "tags": [ - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 903, - "tag": "ModelDB:3658" - } - ], - "timestamp_created": "2024-01-09 11:31:55.544255+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3658", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "433": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 433, - "name": "Neocortical Layer I: I-A and I-K (Zhou, Hablitz 1996)", - "repository_type": "github", - "summary": "NEURON mod files for the I-A and I-K currents from the paper:\r\nZhou FM, Hablitz JJ.\r\nLayer I neurons of the rat neocortex. II. Voltage-dependent outward currents.\r\nJ Neurophysiol 1996 76:668-82.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 904, - "tag": "ModelDB:3660" - } - ], - "timestamp_created": "2024-01-09 11:31:56.159312+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3660", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "434": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 434, - "name": "Olfactory Periglomerular Cells: I-h kinetics (Cadetti, Belluzzi 2001)", - "repository_type": "github", - "summary": "NEURON mod files for the Ih current from the paper:\r\nCadetti L, Belluzzi O.\r\nHyperpolarisation-activated current in glomerular cells \r\nof the rat olfactory bulb.\r\nNeuroreport 12:3117-20 (2001).", - "tags": [ - { - "id": 594, - "tag": "I h" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 905, - "tag": "ModelDB:3665" - } - ], - "timestamp_created": "2024-01-09 11:31:56.689148+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3665", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "435": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 435, - "name": "Thalamic Reticular Network (Destexhe et al 1994)", - "repository_type": "github", - "summary": "Demo for simulating networks of thalamic reticular neurons (reproduces figures from Destexhe A et al 1994)", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 731, - "tag": "I CAN" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 592, - "tag": "Sleep" - }, - { - "id": 593, - "tag": "Spindles" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 732, - "tag": "Tutorial/Teaching" - }, - { - "id": 906, - "tag": "ModelDB:3670" - } - ], - "timestamp_created": "2024-01-09 11:31:57.180542+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3670", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "436": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 436, - "name": "Salamander retinal ganglion cell: ion channels (Fohlmeister, Miller 1997)", - "repository_type": "github", - "summary": "A realistic five (5) channel spiking model reproduces \r\nthe bursting behavior of tiger salamander\r\nganglion cells in the retina.\r\nPlease see the readme for more information.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 907, - "tag": "ModelDB:3673" - } - ], - "timestamp_created": "2024-01-09 11:31:57.766104+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3673", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "437": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 437, - "name": "Ephaptic interactions in olfactory nerve (Bokil et al 2001)", - "repository_type": "github", - "summary": "Bokil, H., Laaris, N., Blinder, K., Ennis, M., and Keller, A. (2001) Ephaptic interactions in the mammalian olfactory system. J. Neurosci. 21:RC173(1-5)", - "tags": [ - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 733, - "tag": "Ephaptic coupling" - }, - { - "id": 734, - "tag": "Extracellular Fields" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 908, - "tag": "ModelDB:3676" - } - ], - "timestamp_created": "2024-01-09 11:31:58.277184+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3676", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "438": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 438, - "name": "Pyramidal Neuron Deep: attenuation in dendrites (Stuart, Spruston 1998)", - "repository_type": "github", - "summary": "Stuart, G. and Spruston, N. Determinants of voltage attenuation in neocortical pyramidal neuron dendrites. Journal of Neuroscience 18:3501-3510, 1998.", - "tags": [ - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 909, - "tag": "ModelDB:3677" - } - ], - "timestamp_created": "2024-01-09 11:31:58.796078+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3677", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "439": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 439, - "name": "Visual Cortex Neurons: Dendritic computations (Archie, Mel 2000)", - "repository_type": "github", - "summary": "Neuron and C program files from Archie, K.A. and Mel, B.W. A model of intradendritic computation of binocular disparity. Nature Neuroscience 3:54-63, 2000\r\nThe original files for this model are located at \r\nthe web site http://www-lnc.usc.edu/~karchie/synmap", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 726, - "tag": "Vision" - }, - { - "id": 910, - "tag": "ModelDB:3682" - } - ], - "timestamp_created": "2024-01-09 11:31:59.270561+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3682", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "440": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 440, - "name": "Thalamic Relay Neuron: I-h (McCormick, Pape 1990)", - "repository_type": "github", - "summary": "NEURON mod files for the Ih current from the paper:\r\nMcCormick DA, Pape HC.\r\nProperties of a hyperpolarization-activated cation current \r\nand its role in rhythmic oscillation in thalamic relay neurones.\r\nJ. Physiol. 1990 431:291-318.", - "tags": [ - { - "id": 594, - "tag": "I h" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 911, - "tag": "ModelDB:3684" - } - ], - "timestamp_created": "2024-01-09 11:31:59.848839+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3684", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "441": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 441, - "name": "Arteriolar networks: Spread of potential (Crane et al 2001)", - "repository_type": "github", - "summary": "Crane, G.J., Hines, M.L., and Neild, T.O. (2001)\r\nSimulating the spread of membrane potential changes in arteriolar networks.\r\nMicrocirculation 8:33-43.\r\n\r\nThis model uses a gap junction density mechanism\r\nto couple arteriolar smooth muscle and endothelium\r\nin microvascular trees.", - "tags": [ - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 912, - "tag": "ModelDB:3785" - } - ], - "timestamp_created": "2024-01-09 11:32:00.325593+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3785", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "442": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 442, - "name": "Olfactory Mitral cell: AP initiation modes (Chen et al 2002)", - "repository_type": "github", - "summary": "The mitral cell primary dendrite plays an important role in transmitting distal olfactory nerve input from olfactory glomerulus to the soma-axon initial segment. To understand how dendritic active properties are involved in this transmission, we have combined dual soma and dendritic patch recordings with computational modeling to analyze action-potential initiation and propagation in the primary dendrite.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 913, - "tag": "ModelDB:3793" - } - ], - "timestamp_created": "2024-01-09 11:32:00.788648+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3793", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "443": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 443, - "name": "Demyelinated and remyelinating axon conductances (Hines, Shrager 1991)", - "repository_type": "github", - "summary": "Hines, Michael and Peter Shrager (1991). A computational test of\r\nthe requirements for conduction in demyelinated axons.\r\nJ. Restorative Neurology and Neuroscience. 3 81--93.", - "tags": [ - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 735, - "tag": "Multiple sclerosis" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 914, - "tag": "ModelDB:3798" - } - ], - "timestamp_created": "2024-01-09 11:32:01.261043+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3798", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "444": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 444, - "name": "Cardiac Atrial Cell (Courtemanche et al 1998)", - "repository_type": "github", - "summary": "Marc Courtemanche, Rafael J. Ramirez, and Stanley Nattel.\r\nIonic mechanisms underlying human atrial action potential properties\r\ninsights from a mathematical model\r\nAm J Physiol Heart Circ Physiol 1998 275: H301-H321.\r\n\r\nThe implementation of this model in NEURON\r\nwas contributed by Ingemar Jacobson.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 737, - "tag": "Heart disease" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 738, - "tag": "Na/Ca exchanger" - }, - { - "id": 915, - "tag": "ModelDB:3800" - } - ], - "timestamp_created": "2024-01-09 11:32:01.747627+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3800", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "445": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 445, - "name": "Dentate Basket Cell: spatial summation of inhibitory synaptic inputs (Bartos et al 2001)", - "repository_type": "github", - "summary": "Spatial summation of inhibitory synaptic input in a passive model of a basket cell from the dentate gyrus of rat hippocampus. Reproduces Figs. 5Ac and d in Bartos, M., Vida, I., Frotscher, M., Geiger, J.R.P, and Jonas, P.. Rapid signaling at inhibitory synapses in a dentate gyrus interneuron network. Journal of Neuroscience 21:2687-2698, 2001.", - "tags": [ - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 916, - "tag": "ModelDB:3801" - } - ], - "timestamp_created": "2024-01-09 11:32:02.225870+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3801", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "446": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 446, - "name": "Retinal Photoreceptor: I Potassium (Beech, Barnes 1989)", - "repository_type": "github", - "summary": "NEURON mod files for a Potassium current from the paper:\r\nBeech DJ, Barnes S.\r\nCharacterization of a voltage-gated K+ channel that accelerates \r\nthe rod response to dim light.\r\nNeuron 3:573-81 (1989).", - "tags": [ - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 917, - "tag": "ModelDB:3802" - } - ], - "timestamp_created": "2024-01-09 11:32:02.797102+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3802", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "447": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 447, - "name": "Spinal Motor Neuron (Dodge, Cooley 1973)", - "repository_type": "github", - "summary": "\"The excitability of various regions of the spinal motorneuron can be specified by solving the partial differential equation of a nerve fiber whose diameter and membrane properties vary with distance. For our model geometrical factors for the myelinated axon, initial segment and cell body were derived from anatomical measurements, the dendritic tree was represented by its equivalent cylinder, and the current-voltage relations of the membrane were described by a modification of the Hodgkin-Huxley model that fits voltage-clamp data from the motorneuron. ...\"", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 732, - "tag": "Tutorial/Teaching" - }, - { - "id": 918, - "tag": "ModelDB:3805" - } - ], - "timestamp_created": "2024-01-09 11:32:03.392817+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3805", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "448": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 448, - "name": "Leech Mechanosensory Neurons: Synaptic Facilitation by Reflected APs (Baccus 1998)", - "repository_type": "github", - "summary": "This model by Stephen Baccus explores the phenomena of action potential (AP) propagation at branch boints in axons. APs are sometimes transmitted down the efferent processes and sometimes are reflected back to the axon of AP origin or neither. See the paper for details. The model zip file contains a readme.txt which list introductory steps to follow to run the simulation. Stephen Baccus's email address: baccus@fas.harvard.edu", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 723, - "tag": "Facilitation" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 719, - "tag": "Pattern Recognition" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 919, - "tag": "ModelDB:3807" - } - ], - "timestamp_created": "2024-01-09 11:32:03.962481+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3807", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "449": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 449, - "name": "MyFirstNEURON (Houweling, Sejnowski 1997)", - "repository_type": "github", - "summary": "MyFirstNEURON is a NEURON demo by Arthur Houweling and Terry Sejnowski. Perform experiments from the book 'Electrophysiology of the Neuron, A Companion to Shepherd's Neurobiology, An Interactive Tutorial' by John Huguenard & David McCormick, Oxford University Press 1997, or design your own one or two cell simulation.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 731, - "tag": "I CAN" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 592, - "tag": "Sleep" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 732, - "tag": "Tutorial/Teaching" - }, - { - "id": 920, - "tag": "ModelDB:3808" - } - ], - "timestamp_created": "2024-01-09 11:32:04.618344+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3808", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "450": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 450, - "name": "Spinal Motor Neuron (McIntyre et al 2002)", - "repository_type": "github", - "summary": "Simulation of peripheral nervous system (PNS) mylelinated axon.\r\n\r\nThis model is described in detail in:\r\n\r\nMcIntyre CC, Richardson AG, and Grill WM.(2002)", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 734, - "tag": "Extracellular Fields" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 921, - "tag": "ModelDB:3810" - } - ], - "timestamp_created": "2024-01-09 11:32:05.201465+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3810", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "451": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 451, - "name": "Visual Cortex Neurons: Dendritic study (Anderson et al 1999)", - "repository_type": "github", - "summary": "Neuron mod and hoc files for the paper: Anderson, J.C. Binzegger, T., Kahana, O., Segev, I., and Martin, K.A.C Dendritic asymmetry cannot account for directional responses in visual cortex. Nature Neuroscience 2:820:824, 1999", - "tags": [ - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 719, - "tag": "Pattern Recognition" - }, - { - "id": 922, - "tag": "ModelDB:3812" - } - ], - "timestamp_created": "2024-01-09 11:32:05.666365+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3812", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "452": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 452, - "name": "Synaptic plasticity: pyramid->pyr and pyr->interneuron (Tsodyks et al 1998)", - "repository_type": "github", - "summary": "An implementation of a model of short-term synaptic plasticity with NEURON. The model was originally described by Tsodyks et al., who assumed that the synapse acted as a current source, but this implementation treats it as a conductance change.\r\n\r\nTsodyks, M., Pawelzik, K., Markram, H.\r\n Neural networks with dynamic synapses.\r\n Neural Computation 10:821-835, 1998.\r\nTsodyks, M., Uziel, A., Markram, H.\r\n Synchrony generation in recurrent networks with\r\n frequency-dependent synapses.\r\n J. Neurosci. 2000 RC50.", - "tags": [ - { - "id": 722, - "tag": "Depression" - }, - { - "id": 723, - "tag": "Facilitation" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - }, - { - "id": 923, - "tag": "ModelDB:3815" - } - ], - "timestamp_created": "2024-01-09 11:32:06.132727+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3815", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "453": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 453, - "name": "Pyramidal Neuron: Deep, Thalamic Relay and Reticular, Interneuron (Destexhe et al 1998, 2001)", - "repository_type": "github", - "summary": "This package shows single-compartment models of different classes of cortical neurons, such as the \"regular-spiking\", \"fast-spiking\" and \"bursting\" (LTS) neurons. The mechanisms included are the Na+ and K+ currents for generating action potentials (INa, IKd), the T-type calcium current (ICaT), and a slow voltage-dependent K+ current (IM). See http://cns.fmed.ulaval.ca/alain_demos.html", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 924, - "tag": "ModelDB:3817" - } - ], - "timestamp_created": "2024-01-09 11:32:06.823116+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/3817", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "454": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 454, - "name": "Squid axon (Hodgkin, Huxley 1952) (NEURON)", - "repository_type": "github", - "summary": "The classic HH model of squid axon membrane\r\nimplemented in NEURON.\r\nHodgkin, A.L., Huxley, A.F. (1952)", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 925, - "tag": "ModelDB:5426" - } - ], - "timestamp_created": "2024-01-09 11:43:23.385133+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/5426", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "455": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 455, - "name": "Midbrain dopamine neuron: firing patterns (Canavier 1999)", - "repository_type": "github", - "summary": "Sodium dynamics drives the generation of\r\nslow oscillations postulated to underly\r\nNMDA-evoked bursting activity.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 740, - "tag": "Na/K pump" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 741, - "tag": "Sodium pump" - }, - { - "id": 926, - "tag": "ModelDB:6763" - } - ], - "timestamp_created": "2024-01-09 11:43:23.995409+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/6763", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "456": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 456, - "name": "Cerebellar purkinje cell (De Schutter and Bower 1994)", - "repository_type": "github", - "summary": "Tutorial simulation of a cerebellar Purkinje cell.\r\n\r\nThis tutorial is based upon a GENESIS simulation of a cerebellar Purkinje cell, modeled and fine-tuned by Erik de Schutter. The tutorial assumes that you have a basic knowledge of the Purkinje cell and its synaptic inputs. It gives visual insight in how different properties as concentrations and channel conductances vary and interact within a real Purkinje cell.", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 742, - "tag": "I p,q" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 732, - "tag": "Tutorial/Teaching" - }, - { - "id": 927, - "tag": "ModelDB:7176" - } - ], - "timestamp_created": "2024-01-09 11:43:24.529367+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/7176", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "457": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 457, - "name": "CA1 pyramidal neuron: conditional boosting of dendritic APs (Watanabe et al 2002)", - "repository_type": "github", - "summary": "Model files from the paper Watanabe S, Hoffman DA, Migliore M,\r\nJohnston D (2002). The experimental and modeling results support the\r\nhypothesis that\r\ndendritic K-A channels and the boosting of back-propagating action\r\npotentials\r\ncontribute to the induction of LTP in CA1 neurons.\r\n See the paper for details.\r\nQuestions about the model may be addressed to Michele Migliore:\r\n michele.migliore@pa.ibf.cnr.it", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 595, - "tag": "Coincidence Detection" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 928, - "tag": "ModelDB:7386" - } - ], - "timestamp_created": "2024-01-09 11:43:25.036373+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/7386", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "458": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 458, - "name": "Feedforward heteroassociative network with HH dynamics (Lytton 1998)", - "repository_type": "github", - "summary": "Using the original McCulloch-Pitts notion of simple on and off spike coding in lieu of rate coding, an Anderson-Kohonen artificial neural network (ANN) associative memory model was ported to a neuronal network with Hodgkin-Huxley dynamics.", - "tags": [ - { - "id": 718, - "tag": "Attractor Neural Network" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 719, - "tag": "Pattern Recognition" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 929, - "tag": "ModelDB:7399" - } - ], - "timestamp_created": "2024-01-09 11:43:25.528867+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/7399", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "459": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 459, - "name": "Hippocampus temporo-septal engram shift model (Lytton 1999)", - "repository_type": "github", - "summary": "Temporo-septal engram shift model of hippocampal memory. The model posits that memories gradually move along the hippocampus from a temporal encoding site to ever more septal sites from which they are recalled. We propose that the sense of time is encoded by the location of the engram along the temporo-septal axis.", - "tags": [ - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 719, - "tag": "Pattern Recognition" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 930, - "tag": "ModelDB:7400" - } - ], - "timestamp_created": "2024-01-09 11:43:26.022485+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/7400", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "460": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 460, - "name": "CA1 Pyramidal Neuron: Synaptic Scaling (London, Segev 2001)", - "repository_type": "github", - "summary": "London and Segev (2001) discuss location dependent and location independent synaptic scaling in a model CA1 neuron with passive dendrites. The freely available text is followed by a critique by Maggee and Cook who comment that\r\nthe London and Segev model is accurate and informative and however needs to be augmented by\r\nactive channels in dendrites. Note: the\r\nzip files for this model are stored at the nature neuroscience website - Click above Supplementary Source Code in the readme.html in the model files", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 743, - "tag": "NEURON (web link to model)" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 931, - "tag": "ModelDB:7485" - } - ], - "timestamp_created": "2024-01-09 11:43:26.568309+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/7485", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "461": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 461, - "name": "CA1 pyramidal neuron: Synaptic Scaling (Magee, Cook 2000)", - "repository_type": "github", - "summary": "Jeffrey Magee and Erik Cook found evidence in experiments and modeling that support the hypothesis that an increase in synaptic conductance for\r\nsynapses at larger distances from the soma is\r\nresponsible for reducing the location dependence (relative to the soma) of synapses.", - "tags": [ - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 932, - "tag": "ModelDB:7509" - } - ], - "timestamp_created": "2024-01-09 11:43:27.033830+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/7509", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "462": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 462, - "name": "Modulation of temporal integration window (Migliore, Shepherd 2002)", - "repository_type": "github", - "summary": "Model simulation file from the paper \r\nM.Migliore and Gordon M. Shepherd\r\nEmerging rules for distributions of active dendritic properties underlying specific neuronal functions. Nature Rev. Neurosci. 3, 362-370 (2002).", - "tags": [ - { - "id": 595, - "tag": "Coincidence Detection" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 933, - "tag": "ModelDB:7659" - } - ], - "timestamp_created": "2024-01-09 11:43:27.498838+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/7659", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "463": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 463, - "name": "Dendritica (Vetter et al 2001)", - "repository_type": "github", - "summary": "Dendritica is a collection of programs for relating dendritic geometry and signal propagation. The programs are based on those used for the simulations described in: Vetter, P., Roth, A. & Hausser, M. (2001) For reprint requests\r\nand additional information please contact Dr. M. Hausser, email address: m.hausser@ucl.ac.uk", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 742, - "tag": "I p,q" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 934, - "tag": "ModelDB:7907" - } - ], - "timestamp_created": "2024-01-09 11:43:28.125956+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/7907", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "464": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 464, - "name": "NMDA receptor saturation (Chen et al 2001)", - "repository_type": "github", - "summary": "Experiments and modeling reported in the paper Chen N, Ren J, Raymond LA, and Murphy T (2001) support the hypothesis that glutamate has a relatively lower potency at NMDARs than previously thought from agonist application under equilibrium conditions. Further information and reprint requests are available from Dr T.H. Murphy thmurphy at interchange.ubc.ca", - "tags": [ - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 935, - "tag": "ModelDB:7988" - } - ], - "timestamp_created": "2024-01-09 11:43:28.647865+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/7988", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "465": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 465, - "name": "Fluctuating synaptic conductances recreate in-vivo-like activity (Destexhe et al 2001)", - "repository_type": "github", - "summary": "This model (and experiments) reported in Destexhe, Rudolh, Fellous, and Sejnowski (2001) support the hypothesis that many of the\r\ncharacteristics of cortical neurons in vivo can be explained by fast glutamatergic and GABAergic conductances varying stochastically.\r\n\r\nSome of these cortical neuron characteristics of fluctuating synaptic origin are a depolarized membrane potential, the\r\npresence of high-amplitude membrane potential fluctuations, a low input resistance and irregular spontaneous firing activity. In addition, the\r\npoint-conductance model could simulate the enhancement of responsiveness due to background activity.\r\nFor more information please contact Alain Destexhe. email: Destexhe@iaf.cnrs-gif.fr", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 744, - "tag": "Synaptic noise" - }, - { - "id": 936, - "tag": "ModelDB:8115" - } - ], - "timestamp_created": "2024-01-09 11:43:29.126588+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/8115", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "466": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 466, - "name": "Spike Initiation in Neocortical Pyramidal Neurons (Mainen et al 1995)", - "repository_type": "github", - "summary": "This model reproduces figure 3A from the paper\r\nMainen ZF, Joerges J, Huguenard JR, Sejnowski TJ (1995). Please see the paper for detail whose full text is available at http://www.cnl.salk.edu/~zach/methods.html\r\nEmail Zach Mainen for questions: mainen@cshl.org", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 937, - "tag": "ModelDB:8210" - } - ], - "timestamp_created": "2024-01-09 11:43:29.621785+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/8210", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "467": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 467, - "name": "Febrile seizure-induced modifications to Ih (Chen et al 2001)", - "repository_type": "github", - "summary": "Modeling and experiments in the paper Chen K,Aradi I, Thom N,Eghbal-Ahmadi M, Baram TZ, and Soltesz I (2001) support the hypothesis that modified Ih currents strongly influence inhibitory inputs in CA1 cells and that the depolarizing shift in Ih activation plays a primary role in this process.\r\nPlease see the paper for details. Some modeling details are available at http://www.ucihs.uci.edu/anatomy/soltesz/supp.htm Correspondance should be addressed to isoltesz@uci.edu (modeling was done by Ildiko Aradi, iaradi@uci.edu)", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 573, - "tag": "Rebound firing" - }, - { - "id": 938, - "tag": "ModelDB:8284" - } - ], - "timestamp_created": "2024-01-09 11:43:30.127874+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/8284", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "468": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 468, - "name": "CA1 pyramidal neuron: effects of Lamotrigine on dendritic excitability (Poolos et al 2002)", - "repository_type": "github", - "summary": "NEURON mod files from N. Poolos, M. Migliore, and D. Johnston, Nature Neuroscience (2002).\r\nThe experimental and modeling results in this paper demonstrate for the first time that neuronal excitability can be altered by pharmaceuticals acting selectively on dendrites, and suggest an important role for Ih in controlling dendritic excitability and epileptogenesis.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 939, - "tag": "ModelDB:9769" - } - ], - "timestamp_created": "2024-01-09 11:43:30.607568+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/9769", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "469": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 469, - "name": "Myelinated axon conduction velocity (Brill et al 1977)", - "repository_type": "github", - "summary": "Examines conduction velocity as function of\r\ninternodal length.", - "tags": [ - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 940, - "tag": "ModelDB:9848" - } - ], - "timestamp_created": "2024-01-09 11:43:31.108694+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/9848", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "470": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 470, - "name": "Temperature-Sensitive conduction at axon branch points (Westerfield et al 1978)", - "repository_type": "github", - "summary": "Propagation of impulses through branching regions of squid axons was examined experimentally and with computer simulations. The ratio of postbranch/prebranch diameters at which propagation failed was very sensitive to temperature.", - "tags": [ - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 745, - "tag": "Conduction failure" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 941, - "tag": "ModelDB:9849" - } - ], - "timestamp_created": "2024-01-09 11:43:31.659831+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/9849", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "471": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 471, - "name": "Conduction in uniform myelinated axons (Moore et al 1978)", - "repository_type": "github", - "summary": "Examines the relative sensitivity of the velocity of impulse propagation to changes in nodal and internodal parameters.", - "tags": [ - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 735, - "tag": "Multiple sclerosis" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 942, - "tag": "ModelDB:9851" - } - ], - "timestamp_created": "2024-01-09 11:43:32.130149+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/9851", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "472": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 472, - "name": "Site of impulse initiation in a neuron (Moore et al 1983)", - "repository_type": "github", - "summary": "Examines the effect of temperature, the taper of the axon hillock, and HH channel density on antidromic spike invasion into the soma and spike initiation under dendritic stimulation.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 943, - "tag": "ModelDB:9852" - } - ], - "timestamp_created": "2024-01-09 11:43:32.591610+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/9852", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "473": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 473, - "name": "Current flow during PAP in squid axon at diameter change (Joyner et al 1980)", - "repository_type": "github", - "summary": "From the paper abstract: An impulse ... sees an increased electrical load at regions of increasing diameter or at branch points with certain morphologies. We present here theoretical and experimental studies on the changes in membrane current and axial current associated with diameter changes. The theoretical studies were done with numerical solutions for cable equations that were generalized to include a varying diameter; the Hodgkin-Huxley equations were used to represent the membrane properties. ... As an action potential approaches a region of increased electrical load, the action potential amplitude and rate of rise decrease, but there is a marked increase in the magnitude of the inward sodium current. ... (See paper for more details.)", - "tags": [ - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 944, - "tag": "ModelDB:9853" - } - ], - "timestamp_created": "2024-01-09 11:43:33.055331+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/9853", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "474": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 474, - "name": "Presynaptic calcium dynamics at neuromuscular junction (Stockbridge, Moore 1984)", - "repository_type": "github", - "summary": "The diffusion of calcium is effectively reduced\r\nby the ratio of bound to free calcium. Treating\r\nthe release magnitude as proportional to the\r\nfourth power of calcium concentration next to\r\nthe membrane gives reasonable facilitation\r\nwith very little release between spikes.", - "tags": [ - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 723, - "tag": "Facilitation" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 945, - "tag": "ModelDB:9888" - } - ], - "timestamp_created": "2024-01-09 11:43:33.516572+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/9888", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "475": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 475, - "name": "Thalamic quiescence of spike and wave seizures (Lytton et al 1997)", - "repository_type": "github", - "summary": "A phase plane analysis of a two cell interaction between a thalamocortical neuron (TC) and a thalamic reticularis neuron (RE).", - "tags": [ - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 946, - "tag": "ModelDB:9889" - } - ], - "timestamp_created": "2024-01-09 11:43:34.004390+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/9889", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "476": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 476, - "name": "Nerve terminal currents at lizard neuromuscular junction (Lindgren, Moore 1989)", - "repository_type": "github", - "summary": "Loose patch clamp measurement of presynaptic ionic currents at lizard neuromuscular junction compared with computer simulations.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 947, - "tag": "ModelDB:10360" - } - ], - "timestamp_created": "2024-01-09 11:43:34.463741+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/10360", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "477": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 477, - "name": "Computer model of clonazepam's effect in thalamic slice (Lytton 1997)", - "repository_type": "github", - "summary": "Demonstration of the effect of a minor pharmacological synaptic\r\nchange at the network level. Clonazepam, a benzodiazepine, enhances\r\ninhibition but is paradoxically useful for certain types of\r\nseizures. This simulation shows how inhibition of\r\ninhibitory cells (the RE cells) produces this counter-intuitive\r\neffect.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 731, - "tag": "I CAN" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 746, - "tag": "Therapeutics" - }, - { - "id": 948, - "tag": "ModelDB:12631" - } - ], - "timestamp_created": "2024-01-09 11:43:34.950060+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/12631", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "478": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 478, - "name": "Thalamic reticular neurons: the role of Ca currents (Destexhe et al 1996)", - "repository_type": "github", - "summary": "The experiments and modeling reported in this paper show how intrinsic bursting properties of RE cells may be explained by dendritic calcium currents.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 949, - "tag": "ModelDB:17663" - } - ], - "timestamp_created": "2024-01-09 11:43:35.496915+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/17663", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "479": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 479, - "name": "Cerebellar purkinje cell: K and Ca channels regulate APs (Miyasho et al 2001)", - "repository_type": "github", - "summary": "We adopted De Schutter and Bower's model as the starting point, then modified the descriptions of\r\nseveral ion channels, such as the P-type Ca channel and the delayed rectifier K channel, and added class-E Ca channels and D-type K channels to the model. Our new model reproduces most of our experimental results and supports the conclusions of our experimental study that class-E Ca channels and D-type K channels are present and functioning in the dendrites of Purkinje neurons.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 747, - "tag": "I_KD" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 950, - "tag": "ModelDB:17664" - } - ], - "timestamp_created": "2024-01-09 11:43:36.017381+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/17664", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "480": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 480, - "name": "Efficient Method for Computing Synaptic Conductance (Destexhe et al 1994)", - "repository_type": "github", - "summary": "A simple model of transmitter release is used to solve first order kinetic equations of neurotransmiter/receptor binding. This method is applied to a glutamate and gabaa receptor. See reference for more details. The method is extended to more complex kinetic schemes in a seperate paper (Destexhe et al J Comp Neuro 1:195-231, 1994). Application to AMPA, NMDA, GABAA, and GABAB receptors is given in a book chapter (Destexhe et al In: The Neurobiology of Computation, Edited by Bower, J., Kluwer Academic Press, Norwell MA, 1995, pp. 9-14.) More information and papers at\r\nhttp://cns.iaf.cnrs-gif.fr/Main.html\r\nand through email: Destexhe@iaf.cnrs-gif.fr", - "tags": [ - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 951, - "tag": "ModelDB:18197" - } - ], - "timestamp_created": "2024-01-09 11:43:36.570300+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/18197", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "481": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 481, - "name": "Application of a common kinetic formalism for synaptic models (Destexhe et al 1994)", - "repository_type": "github", - "summary": "Application to AMPA, NMDA, GABAA, and GABAB receptors is given in a book chapter. The reference paper synthesizes a comprehensive general description of synaptic transmission with Markov kinetic models. This framework is applicable to modeling ion channels, synaptic release, and all receptors. Please see the references for more details. A simple introduction to this method is given in a seperate paper Destexhe et al Neural Comput 6:14-18 , 1994). More information and papers at http://cns.iaf.cnrs-gif.fr/Main.html and through email: Destexhe@iaf.cnrs-gif.fr", - "tags": [ - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 748, - "tag": "Markov-type model" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 952, - "tag": "ModelDB:18198" - } - ], - "timestamp_created": "2024-01-09 11:43:37.030781+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/18198", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "482": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 482, - "name": "Kinetic synaptic models applicable to building networks (Destexhe et al 1998)", - "repository_type": "github", - "summary": "Simplified AMPA, NMDA, GABAA, and GABAB receptor models useful for building networks are described in a book chapter. One reference paper synthesizes a comprehensive general description of synaptic transmission with Markov kinetic models which is applicable to modeling ion channels, synaptic release, and all receptors. Also a simple introduction to this method is given in a seperate paper Destexhe et al Neural Comput 6:14-18 , 1994). More information and papers at http://cns.iaf.cnrs-gif.fr/Main.html and through email: Destexhe@iaf.cnrs-gif.fr", - "tags": [ - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 748, - "tag": "Markov-type model" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 953, - "tag": "ModelDB:18500" - } - ], - "timestamp_created": "2024-01-09 11:43:37.500703+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/18500", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "483": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 483, - "name": "Salamander retinal ganglian cells: morphology influences firing (Sheasby, Fohlmeister 1999)", - "repository_type": "github", - "summary": "Nerve impulse entrainment and other\r\nexcitation and passive phenomena are analyzed for a morphologically\r\ndiverse and exhaustive data set (n=57) of realistic (3-dimensional\r\ncomputer traced) soma-dendritic tree structures of ganglion cells in\r\nthe tiger salamander (Ambystoma tigrinum) retina.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 595, - "tag": "Coincidence Detection" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 954, - "tag": "ModelDB:18501" - } - ], - "timestamp_created": "2024-01-09 11:43:37.996785+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/18501", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "484": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 484, - "name": "Spine fusion and branching affects synaptic response (Rusakov et al 1996, 1997)", - "repository_type": "github", - "summary": "This compartmental model of a hippocampal granule cell has spinous synapses\r\nplaced on the second-order dendrites. Changes in shape and connectivity of\r\nthe spines usually does not effect the synaptic response of the cell unless\r\nactive conductances are incorporated into the spine membrane (e.g. voltage-dependent\r\nCa2+ channels). With active conductances, spines can generate spike-like events.\r\nWe showed that changes like fusion and branching, or in fact any increase in the \r\nequivalent spine neck resistance, could trigger a dramatic increase in the spine's\r\ninfluence on the dendritic shaft potential.", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 955, - "tag": "ModelDB:18502" - } - ], - "timestamp_created": "2024-01-09 11:43:38.524891+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/18502", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "485": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 485, - "name": "Dendritic tip geometry effects electrical properties (Tsutsui, Oka 2001)", - "repository_type": "github", - "summary": "In their teleost thalamic neuron models the authors demonstrate a dramatic increase in the passive propagation of synaptic inputs through the dendritic stalk to the soma in cells with larger tips.", - "tags": [ - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 956, - "tag": "ModelDB:18738" - } - ], - "timestamp_created": "2024-01-09 11:43:39.055221+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/18738", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "486": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 486, - "name": "Novel Na current with slow de-inactivation (Tsutsui, Oka 2002)", - "repository_type": "github", - "summary": "The authors found a novel Na current in teleost thalamic nuclei was well described by the m^3 h Hodgkin-Huxley model. The kinetic parameters derived from their experiments (see the reference for details) revealed that the h gate had a large time constant\r\n(~100ms at -80 to -50mV). This explains the thalamic neurons long refractory period and the gradual recovery of AP amplitude as the inter spike interval grows.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 957, - "tag": "ModelDB:18742" - } - ], - "timestamp_created": "2024-01-09 11:43:39.526581+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/18742", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "487": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 487, - "name": "Emergent properties of networks of biological signaling pathways (Bhalla, Iyengar 1999)", - "repository_type": "github", - "summary": "Biochemical signaling networks were constructed with experimentally obtained constants and analyzed by computational methods to understand their role in complex biological processes. These networks exhibit emergent properties such as integration of signals across multiple time scales, generation of distinct outputs depending on input strength and duration, and self-sustaining feedback loops. Properties of signaling networks raise the possibility that information for \"learned behavior\" of biological systems may be stored within intracellular biochemical reactions that comprise signaling pathways.", - "tags": [ - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 750, - "tag": "GENESIS (web link to model)" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - }, - { - "id": 751, - "tag": "Signaling pathways" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 958, - "tag": "ModelDB:18871" - } - ], - "timestamp_created": "2024-01-09 11:43:40.107451+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/18871", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "488": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 488, - "name": "Fast AMPA receptor signaling (Geiger et al 1997)", - "repository_type": "github", - "summary": "Glutamatergic transmission at a principal neuron-interneuron synapse was investigated by dual whole-cell patch-clamp recording in rat hippocampal slices combined with morphological analysis and modeling. Simulations based on a compartmental model of the interneuron indicated that the rapid postsynaptic conductance change determines the shape and the somatodendritic integration of EPSPs, thus enabling interneurons to detect synchronous principal neuron activity.", - "tags": [ - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 959, - "tag": "ModelDB:19022" - } - ], - "timestamp_created": "2024-01-09 11:43:40.797330+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/19022", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "489": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 489, - "name": "Activity dependent regulation of pacemaker channels by cAMP (Wang et al 2002)", - "repository_type": "github", - "summary": "Demonstration of the physiological consequences of the cyclic allosteric gating scheme for Ih mediated by HCN2 in thalamocortical relay cells.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 960, - "tag": "ModelDB:19176" - } - ], - "timestamp_created": "2024-01-09 11:43:41.429329+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/19176", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "490": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 490, - "name": "Geometry-induced features of current transfer in neuronal dendrites (Korogod, Kulagina 1998)", - "repository_type": "github", - "summary": "The impact of dendritic geometry on somatopetal transfer of the current generated by steady uniform activation of excitatory synaptic conductance distributed over passive, or active (Hodgkin-Huxley type), dendrites was studied in simulated neurons.", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 961, - "tag": "ModelDB:19214" - } - ], - "timestamp_created": "2024-01-09 11:43:42.064556+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/19214", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "491": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 491, - "name": "Conditions of dominant effectiveness of distal dendrites (Korogod, Kulagina 1998)", - "repository_type": "github", - "summary": "The model illustrates and explains bistable spatial patterns of the current transfer effectiveness \r\nin the active dendrite with distributed (multiple) tonic excitatory, NMDA type, synaptic input.", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 962, - "tag": "ModelDB:19366" - } - ], - "timestamp_created": "2024-01-09 11:43:42.510798+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/19366", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "492": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 492, - "name": "Dendritic L-type Ca currents in motoneurons (Carlin et al 2000)", - "repository_type": "github", - "summary": "A component of recorded currents demonstrated kinetics consistent with a current originating at a site spatially segregated from the soma. In response to step commands this component was seen as a late-onset, low amplitude persistent current whilst in response to depolarizing-repolarizing ramp commands a low voltage clockwise current hysteresis was recorded. Simulations using a neuromorphic motoneuron model could reproduce these currents only if a noninactivating calcium conductance was placed in the dendritic compartments.", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 752, - "tag": "Dendritic Bistability" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 963, - "tag": "ModelDB:19491" - } - ], - "timestamp_created": "2024-01-09 11:43:42.974042+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/19491", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "493": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 493, - "name": "Synaptic integration in a model of granule cells (Gabbiani et al 1994)", - "repository_type": "github", - "summary": "We have developed a compartmental model of a turtle cerebellar granule cell consisting of 13 compartments that represent the soma and 4 dendrites. We used this model to investigate the synaptic integration of mossy fiber inputs in granule cells. See reference or abstract at PubMed link below for more information.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 595, - "tag": "Coincidence Detection" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 964, - "tag": "ModelDB:19591" - } - ], - "timestamp_created": "2024-01-09 11:43:43.456392+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/19591", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "494": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 494, - "name": "CA1 pyramidal neuron: integration of subthreshold inputs from PP and SC (Migliore 2003)", - "repository_type": "github", - "summary": "The model shows how the experimentally observed increase in the dendritic density of Ih and IA could have a major role in constraining the temporal integration window for the main CA1 \r\nsynaptic inputs.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 595, - "tag": "Coincidence Detection" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 965, - "tag": "ModelDB:19696" - } - ], - "timestamp_created": "2024-01-09 11:43:43.947894+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/19696", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "495": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 495, - "name": "Leech heart interneuron network model (Hill et al 2001, 2002)", - "repository_type": "github", - "summary": "We have created a computational model of the timing network that paces the heartbeat of the medicinal leech, Hirudo medicinalis. In the intact nerve cord, segmental oscillators are mutually entrained to the same cycle period. Although experiments have shown that the segmental oscillators are coupled by inhibitory coordinating interneurons, the underlying mechanisms of intersegmental coordination have not yet been elucidated. To help understand this coordination, we have created a simple computational model with two variants: symmetric and asymmetric. See references for more details. Biologically realistic network models with two, six, and eight cells and a tutorial are available at the links to Calabrese's web site below.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 732, - "tag": "Tutorial/Teaching" - }, - { - "id": 966, - "tag": "ModelDB:19698" - } - ], - "timestamp_created": "2024-01-09 11:43:44.451770+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/19698", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "496": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 496, - "name": "Signal integration in a CA1 pyramidal cell (Graham 2001)", - "repository_type": "github", - "summary": "This model investigates signal integration in the dendritic tree of a hippocampal CA1 pyramidal cell when different combinations of active channels are present in the tree (Graham, 2001)", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 967, - "tag": "ModelDB:19746" - } - ], - "timestamp_created": "2024-01-09 11:43:44.974883+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/19746", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "497": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 497, - "name": "Synaptic transmission at the calyx of Held (Graham et al 2001)", - "repository_type": "github", - "summary": "This model allows the user to investigate faciliation and depression in a complex Monte Carlo model of the calyx of Held, a giant synapse in the mammalian auditory system (Graham et al, 2001)", - "tags": [ - { - "id": 722, - "tag": "Depression" - }, - { - "id": 723, - "tag": "Facilitation" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 753, - "tag": "Vestibular" - }, - { - "id": 968, - "tag": "ModelDB:19747" - } - ], - "timestamp_created": "2024-01-09 11:43:45.491614+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/19747", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "498": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 498, - "name": "T-type Calcium currents (McRory et al 2001)", - "repository_type": "github", - "summary": "NEURON mod files for CaT currents from the paper\r\nMcRory et al., J.Biol.Chem. 276:3999 (2001).\r\nIn this paper, three members (alpha-1G, -1H, and -1I) of the LVA calcium channels family were studied. Kinetic parameters were derived from functional expression in transfected cells.", - "tags": [ - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 969, - "tag": "ModelDB:19920" - } - ], - "timestamp_created": "2024-01-09 11:43:45.958182+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/19920", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "499": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 499, - "name": "CA3 pyramidal neuron (Lazarewicz et al 2002)", - "repository_type": "github", - "summary": "The model shows how using a CA1-like distribution of active dendritic conductances in a CA3 morphology results in dendritic initiation of spikes during a burst.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 731, - "tag": "I CAN" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 970, - "tag": "ModelDB:20007" - } - ], - "timestamp_created": "2024-01-09 11:43:46.531304+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/20007", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "500": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 500, - "name": "Transfer properties of Neuronal Dendrites (Korogod et al 1998)", - "repository_type": "github", - "summary": "The somatopetal current transfer was studied in mathematical models of a reconstructed brainstem motoneuron with tonically activated excitatory synaptic inputs uniformly distributed over the dendritic arborization. See paper and below readme.txt for more information.", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 971, - "tag": "ModelDB:20014" - } - ], - "timestamp_created": "2024-01-09 11:43:47.050122+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/20014", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "501": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 501, - "name": "CA1 interneuron: K currents (Lien et al 2002)", - "repository_type": "github", - "summary": "NEURON mod files for slow and fast K-DR, and K-A potassium currents in inhibitory interneurones of stratum oriens-alveus of the hippocampal CA1 region.", - "tags": [ - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 972, - "tag": "ModelDB:20015" - } - ], - "timestamp_created": "2024-01-09 11:43:47.541955+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/20015", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "502": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 502, - "name": "CA1 pyramidal neuron: as a 2-layer NN and subthreshold synaptic summation (Poirazi et al 2003)", - "repository_type": "github", - "summary": "We developed a CA1\r\npyramidal cell model calibrated with a broad spectrum of in vitro data. Using simultaneous\r\ndendritic and somatic recordings, and combining results for two different response measures\r\n(peak vs. mean EPSP), two different stimulus formats (single shock vs. 50 Hz trains),\r\nand two different spatial integration conditions (within vs. between-branch summation),\r\nwe found the cell's subthreshold responses to paired inputs are best described as a sum of\r\nnonlinear subunit responses, where the subunits correspond to different dendritic branches.\r\nIn addition to suggesting a new type of experiment and providing testable predictions, our\r\nmodel shows how conclusions regarding synaptic arithmetic can be influenced by an array\r\nof seemingly innocuous experimental design choices.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 754, - "tag": "Delay" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 722, - "tag": "Depression" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 973, - "tag": "ModelDB:20212" - } - ], - "timestamp_created": "2024-01-09 11:43:48.061454+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/20212", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "503": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 503, - "name": "Mechanisms of fast rhythmic bursting in a layer 2/3 cortical neuron (Traub et al 2003)", - "repository_type": "github", - "summary": "This simulation is based on the reference paper listed below.\r\nThis port was made by Roger D Traub and Maciej T Lazarewicz (mlazarew at seas.upenn.edu)\r\n\r\nThanks to Ashlen P Reid for help with porting a morphology of the cell.", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 974, - "tag": "ModelDB:20756" - } - ], - "timestamp_created": "2024-01-09 11:43:48.583686+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/20756", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "504": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 504, - "name": "Gamma oscillations in hippocampal interneuron networks (Bartos et al 2002)", - "repository_type": "github", - "summary": "To examine whether an interneuron network with fast inhibitory synapses can act as a gamma frequency oscillator, we developed an interneuron network model based on experimentally determined properties. In comparison to previous interneuron network models, our model was able to generate oscillatory activity with higher coherence over a broad range of frequencies (20-110 Hz). In this model, high coherence and flexibility in frequency control emerge from the combination of synaptic properties, network structure, and electrical coupling.\r\n", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 975, - "tag": "ModelDB:21329" - } - ], - "timestamp_created": "2024-01-11 10:53:32.240459+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/21329", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "505": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 505, - "name": "Facilitation by residual calcium (Stockbridge, Hines 1982)", - "repository_type": "github", - "summary": "The residual calcium hypothesis is compatible\r\nwith facilitation of transmitter release from\r\nthe neuromuscular junction.", - "tags": [ - { - "id": 723, - "tag": "Facilitation" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 976, - "tag": "ModelDB:21984" - } - ], - "timestamp_created": "2024-01-11 10:53:33.013194+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/21984", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "506": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 506, - "name": "Correcting space clamp in dendrites (Schaefer et al. 2003 and 2007)", - "repository_type": "github", - "summary": "In voltage-clamp experiments, incomplete space clamp distorts the recorded currents, rendering accurate analysis impossible. Here, we present\r\na simple numerical algorithm that corrects such distortions. The method enabled accurate\r\nretrieval of the local densities, kinetics, and density gradients of somatic and dendritic channels. The correction method was applied to two-electrode voltage-clamp recordings of K currents from the apical dendrite of layer 5 neocortical pyramidal\r\nneurons. The generality and robustness of the algorithm make it a useful tool for voltage-clamp analysis of voltage-gated\r\ncurrents in structures of any morphology that is amenable to the voltage-clamp technique.", - "tags": [ - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 977, - "tag": "ModelDB:22203" - } - ], - "timestamp_created": "2024-01-11 10:53:33.895307+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/22203", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "507": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 507, - "name": "Gamma oscillations in hippocampal interneuron networks (Wang, Buzsaki 1996)", - "repository_type": "github", - "summary": "The authors investigated the hypothesis that 20-80Hz neuronal (gamma) oscillations can emerge in sparsely connected network models of GABAergic fast-spiking interneurons. They explore model NN synchronization and compare their results to anatomical and electrophysiological data from hippocampal fast spiking interneurons.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 978, - "tag": "ModelDB:26997" - } - ], - "timestamp_created": "2024-01-11 10:53:34.396628+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/26997", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "508": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 508, - "name": "Sleep-wake transitions in corticothalamic system (Bazhenov et al 2002)", - "repository_type": "github", - "summary": "The authors investigate the transition between sleep and awake states with intracellular recordings in cats and computational models. The model describes many essential features of slow wave sleep and activated states as well as the transition between them.", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 722, - "tag": "Depression" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - }, - { - "id": 592, - "tag": "Sleep" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 979, - "tag": "ModelDB:28189" - } - ], - "timestamp_created": "2024-01-11 10:57:57.254494+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/28189", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "509": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 509, - "name": "Active dendrites and spike propagation in a hippocampal interneuron (Saraga et al 2003)", - "repository_type": "github", - "summary": "We create multi-compartment models of an Oriens-Lacunosum/Moleculare (O-LM) hippocampal interneuron using passive properties, channel kinetics, densities and distributions specific to this cell type, and explore its signaling characteristics. We find that spike initiation depends on both location and amount of input, as well as the intrinsic properties of the interneuron. Distal synaptic input always produces strong back-propagating spikes whereas proximal input could produce both forward and back-propagating spikes depending on the input strength. Please see paper for more details.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 980, - "tag": "ModelDB:28316" - } - ], - "timestamp_created": "2024-01-11 10:57:58.232582+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/28316", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "510": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 510, - "name": "Signal integration in LGN cells (Briska et al 2003)", - "repository_type": "github", - "summary": "Computer models were used to investigate passive properties of lateral geniculate nucleus thalamocortical cells and thalamic\r\ninterneurons based on in vitro whole-cell study. Two neurons of each type were characterized physiologically and morphologically. Differences in the attenuation of propagated signals depend on both cell morphology and signal frequency. See the paper for details.", - "tags": [ - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 726, - "tag": "Vision" - }, - { - "id": 981, - "tag": "ModelDB:29942" - } - ], - "timestamp_created": "2024-01-11 10:57:59.113045+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/29942", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "511": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 511, - "name": "CA1 pyramidal neuron: effects of Ih on distal inputs (Migliore et al 2004)", - "repository_type": "github", - "summary": "NEURON mod files from the paper:\r\nM. Migliore, L. Messineo, M. Ferrante\r\nDendritic Ih selectively blocks temporal summation of unsynchronized distal inputs in CA1 pyramidal neurons, J.Comput. Neurosci. 16:5-13 (2004).\r\nThe model demonstrates how the dendritic Ih in pyramidal neurons could selectively suppress AP generation for a volley of excitatory afferents \r\nwhen they are asynchronously and distally activated.\r\n", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 982, - "tag": "ModelDB:32992" - } - ], - "timestamp_created": "2024-01-11 10:57:59.598573+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/32992", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "512": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 512, - "name": "Irregular oscillations produced by cyclic recurrent inhibition (Friesen, Friesen 1994)", - "repository_type": "github", - "summary": "Model of recurrent cyclic inhibition as described on p.119 of Friesen and Friesen (1994), which was slightly modified from Szekely's model (1965) of a network for producing alternating limb movements.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 983, - "tag": "ModelDB:33728" - } - ], - "timestamp_created": "2024-01-11 10:58:00.138503+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/33728", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "513": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 513, - "name": "Local variable time step method (Lytton, Hines 2005)", - "repository_type": "github", - "summary": "The local variable time-step method utilizes separate variable step integrators for individual neurons in the network. It is most suitable for medium size networks in which average synaptic input intervals to a single cell are much greater than a fixed step dt.", - "tags": [ - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 984, - "tag": "ModelDB:33975" - } - ], - "timestamp_created": "2024-01-11 10:58:00.621326+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/33975", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "514": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 514, - "name": "Serotonergic modulation of Aplysia sensory neurons (Baxter et al 1999)", - "repository_type": "github", - "summary": "The present study investigated how the modulation of these currents altered the spike duration\r\nand excitability of sensory neurons and examined the relative contributions\r\nof PKA- and PKC-mediated effects to the actions of 5-HT. A\r\nHodgkin-Huxley type model was developed that described the ionic\r\nconductances in the somata of sensory neurons. The descriptions of\r\nthese currents and their modulation were based largely on voltageclamp\r\ndata from sensory neurons. Simulations were preformed with\r\nthe program SNNAP (Simulator for Neural Networks and Action\r\nPotentials). The model was sufficient to replicate empirical data that\r\ndescribes the membrane currents, action potential waveform and\r\nexcitability as well as their modulation by application of 5-HT,\r\nincreased levels of adenosine cyclic monophosphate or application of\r\nactive phorbol esters. The results provide\r\nseveral predictions that warrant additional experimental investigation\r\nand illustrate the importance of considering indirect as well as direct\r\neffects of modulatory agents on the modulation of membrane currents. See paper for more details.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 731, - "tag": "I CAN" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 757, - "tag": "SNNAP" - }, - { - "id": 985, - "tag": "ModelDB:33986" - } - ], - "timestamp_created": "2024-01-11 13:52:05.724998+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/33986", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "515": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 515, - "name": "Enhanced Excitability in Hermissenda: modulation by 5-HT (Cai et al 2003)", - "repository_type": "github", - "summary": "Serotonin (5-HT) applied to the exposed but otherwise intact nervous system results in enhanced excitability\r\nof Hermissenda type-B photoreceptors. Several ion currents in the type-B photoreceptors are modulated\r\nby 5-HT, including the A-type K+ current (IK,A), sustained Ca2+ current (ICa,S), Ca-dependent K+ current (IK,Ca),\r\nand a hyperpolarization-activated inward rectifier current (Ih). In this study,we developed a computational model that\r\nreproduces physiological characteristics of type B photoreceptors, e.g. resting membrane potential, dark-adapted\r\nspike activity, spike width, and the amplitude difference between somatic and axonal spikes. We then used the\r\nmodel to investigate the contribution of different ion currents modulated by 5-HT to the magnitudes of enhanced\r\nexcitability produced by 5-HT. See paper for results and more details.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 757, - "tag": "SNNAP" - }, - { - "id": 986, - "tag": "ModelDB:34163" - } - ], - "timestamp_created": "2024-01-11 13:52:06.482802+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/34163", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "516": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 516, - "name": "Burst induced synaptic plasticity in Apysia sensorimotor neurons (Phares et al 2003)", - "repository_type": "github", - "summary": "The Aplysia sensorimotor synapse is a key site of plasticity for several simple forms of learning. Intracellular stimulation of sensory neurons to fire a burst of action potentials at 10 Hz for 1 sec led to significant\r\nhomosynaptic depression of postsynaptic responses. During the burst, the steady-state depressed phase of the postsynaptic response, which was only 20% of the initial EPSP of the burst, still contributed to firing the motor neuron. To explore the functional contribution of transient homosynaptic depression to the response of the motor neuron, computer simulations of the sensorimotor synapse with and without depression were compared. Depression allowed the motor\r\nneuron to produce graded responses over a wide range of presynaptic input strength. \r\nThus, synaptic depression increased the dynamic range of the sensorimotor synapse and can, in principle, have a profound effect on\r\ninformation processing. Please see paper for results and details.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 722, - "tag": "Depression" - }, - { - "id": 723, - "tag": "Facilitation" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 757, - "tag": "SNNAP" - }, - { - "id": 758, - "tag": "Sensory processing" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 987, - "tag": "ModelDB:34168" - } - ], - "timestamp_created": "2024-01-11 13:52:07.218212+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/34168", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "517": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 517, - "name": "Caffeine-induced electrical oscillations in Aplysia neurons (Komendantov, Kononenko 2000)", - "repository_type": "github", - "summary": "It has been found that in cultured Aplysia neurons bath applications of 40 mM cafffeine evokes oscillations of the membrane potential with about a 40 mV amplitude with a frequency of 0.2 to 0.5 Hz. The most probable mechanism of these caffeine-induced oscillations is inhibition of voltage-activated outward potassium current and, as can be seen from our mathematical modeling, slowdown of inactivation of inward sodium current. It seems likely that these oscillations have a purely membrane origin. Please see paper for results and details.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 757, - "tag": "SNNAP" - }, - { - "id": 988, - "tag": "ModelDB:34558" - } - ], - "timestamp_created": "2024-01-11 13:52:07.744081+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/34558", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "518": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 518, - "name": "I A in Kenyon cells resemble Shaker currents (Pelz et al 1999)", - "repository_type": "github", - "summary": "Cultured Kenyon cells from the mushroom body of the honeybee,\r\nApis mellifera, show a voltage-gated, fast transient K1 current that is\r\nsensitive to 4-aminopyridine, an A current. The kinetic properties of\r\nthis A current and its modulation by extracellular K1 ions were\r\ninvestigated in vitro with the whole cell patch-clamp technique. The\r\nA current was isolated from other voltage-gated currents either pharmacologically\r\nor with suitable voltage-clamp protocols. Hodgkin- and\r\nHuxley-style mathematical equations were used for the description of\r\nthis current and for the simulation of action potentials in a Kenyon cell\r\nmodel. The data of the A\r\ncurrent were incorporated into a reduced computational model of the\r\nvoltage-gated currents of Kenyon cells. In addition, the model contained\r\na delayed rectifier K current, a Na current, and a leakage\r\ncurrent. The model reproduces several experimental features and makes \r\npredictions. See paper for details and results.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 757, - "tag": "SNNAP" - }, - { - "id": 989, - "tag": "ModelDB:34560" - } - ], - "timestamp_created": "2024-01-11 13:52:08.281603+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/34560", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "519": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 519, - "name": "A network model of tail withdrawal in Aplysia (White et al 1993)", - "repository_type": "github", - "summary": "The contributions of monosynaptic and polysynaptic circuitry to the tail-withdrawal reflex in the marine mollusk Aplysia californica were assessed by the use of physiologically based neural network models. Effects of monosynaptic circuitry were examined by the use of a two-layer network model with four sensory neurons in the input layer and one motor neuron in the output layer. Results of these simulations indicated that the monosynaptic circuit could not account fully for long-duration responses of tail motor neurons elicited by tail stimulation.\r\nA three-layer network model was constructed by interposing a layer of two excitatory interneurons between the input and output layers of the two-layer network model. The three-layer model could account for long-duration responses in motor neurons. Sensory neurons are a known site of plasticity in Aplysia. Synaptic plasticity at more than one locus modified dramatically the input-output relationship of the three-layer network model. This feature gave the model redundancy in its plastic properties and points to the possibility of distributed memory in the circuitry mediating withdrawal reflexes in Aplysia.\r\nPlease see paper for more results and details.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 723, - "tag": "Facilitation" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 757, - "tag": "SNNAP" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 990, - "tag": "ModelDB:34606" - } - ], - "timestamp_created": "2024-01-11 13:52:08.765057+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/34606", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "520": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 520, - "name": "CA3 pyramidal cell: rhythmogenesis in a reduced Traub model (Pinsky, Rinzel 1994)", - "repository_type": "github", - "summary": "Fig. 2A and 3 are reproduced in this simulation of Pinsky PF, Rinzel J (1994).", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 991, - "tag": "ModelDB:35358" - } - ], - "timestamp_created": "2024-01-11 13:52:09.258486+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/35358", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "521": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 521, - "name": "Stochastic LTP/LTD conditioning of a synapse (Migliore and Lansky 1999)", - "repository_type": "github", - "summary": "Protracted presynaptic activity can induce long-term potentiation\r\n(LTP) or long-term depression (LTD) of the synaptic strength. However,\r\nvirtually all the experiments testing how LTP and LTD depend on the\r\nconditioning input are carried out with trains of stimuli at constant\r\nfrequencies, whereas neurons in vivo most likely experience a stochastic\r\nvariation of interstimulus intervals. We used a computational model of\r\nsynaptic transmission to test if and to what extent the stochastic\r\nfluctuations of an input signal could alter the probability to change the\r\nstate of a synapse. See paper for conclusions.", - "tags": [ - { - "id": 722, - "tag": "Depression" - }, - { - "id": 723, - "tag": "Facilitation" - }, - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 760, - "tag": "Post-Tetanic Potentiation" - }, - { - "id": 761, - "tag": "QBasic/QuickBasic/Turbo Basic/VBA" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 992, - "tag": "ModelDB:35781" - } - ], - "timestamp_created": "2024-01-11 13:52:09.740186+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/35781", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "522": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 522, - "name": "Auditory nerve model for predicting performance limits (Heinz et al 2001)", - "repository_type": "github", - "summary": "A computational auditory nerve (AN) model was developed for\r\nuse in modeling psychophysical experiments with normal and impaired\r\nhuman listeners. In this phenomenological model, many physiologically\r\nvulnerable response properties associated with the cochlear amplifier are\r\nrepresented by a single nonlinear control mechanism, see paper for details. Several model versions are described that\r\ncan be used to evaluate the relative effects of these nonlinear properties.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 762, - "tag": "Audition" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 993, - "tag": "ModelDB:36834" - } - ], - "timestamp_created": "2024-01-11 13:52:10.245357+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/36834", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "523": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 523, - "name": "Model of cochlear membrane adapted (Peterson, Bogert 1950)", - "repository_type": "github", - "summary": "This model, adapted from Peterson and Bogert (1950), simulates the response of the gerbil basilar membrane to a pure tone stimulus. This model does not attempt to simulate the effect of outer hair cell motility. The program prompts the user for the stimulus frequency and the Q (quality factor) for the basilar membrane impedance. It then plots cochlear partition volume velocity, the pressure difference across the partition and the cochlear partition impedance as a function of cochlear location. More information on the actual computations are contained in comments within the m-file.", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 994, - "tag": "ModelDB:36861" - } - ], - "timestamp_created": "2024-01-11 13:52:10.728825+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/36861", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "524": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 524, - "name": "Inner hair cell auditory nerve synapse model (Deligeorges, Mountain 1997)", - "repository_type": "github", - "summary": "This model simulates the response of the synapse between the inner hair cell and an auditory nerve fiber to a square voltage pulse applied to the IHC membrane. The model output is average firing rate. More details of this model can be found in: Deligeorges and Mountain.", - "tags": [ - { - "id": 762, - "tag": "Audition" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 995, - "tag": "ModelDB:36869" - } - ], - "timestamp_created": "2024-01-11 13:52:11.193130+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/36869", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "525": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 525, - "name": "Multiple modes of inner hair cell stimulation (Mountain, Cody 1999)", - "repository_type": "github", - "summary": "This model simulates the membrane potential of an inner hair cell for a sinusoidal stimulus to the hair bundle. It uses a 2-state Boltzmann model for the tension-gated conductance in the stereocilia and a linear model for the basolateral membrane. This model is based on the IHC model used in Mountain and Cody (1999).", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 996, - "tag": "ModelDB:36956" - } - ], - "timestamp_created": "2024-01-11 13:52:11.682311+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/36956", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "526": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 526, - "name": "Auditory nerve response model (Zhang et al 2001)", - "repository_type": "github", - "summary": "A phenomenological model was developed to describe responses of high-spontaneous-rate auditory-nerve (AN) fibers, including several nonlinear response properties. The implementation of this model represents a relatively simple phenomenological description of a single mechanism that underlies several important nonlinear response properties of AN fibers. The model provides a tool for studying the roles of these nonlinearities in the encoding of simple and complex sounds in the responses of populations of AN fibers.", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 997, - "tag": "ModelDB:37103" - } - ], - "timestamp_created": "2024-01-11 13:52:12.149863+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/37103", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "527": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 527, - "name": "Auditory nerve response model (Tan, Carney 2003)", - "repository_type": "github", - "summary": "A computational model was developed to simulate the responses of auditory-nerve (AN) fibers in cat. The incorporation of both the level-independent frequency glide and the level-dependent compressive nonlinearity into a phenomenological model for the AN was the primary focus of this work. The ability of this model to process arbitrary sound inputs makes it a useful tool for studying peripheral auditory processing.", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 998, - "tag": "ModelDB:37129" - } - ], - "timestamp_created": "2024-01-11 13:52:12.645998+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/37129", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "528": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 528, - "name": "Auditory nerve model with linear tuning (Heinz et al 2001)", - "repository_type": "github", - "summary": "A method for calculating psychophysical performance limits based on stochastic \r\nneural responses is introduced and compared to previous analytical methods for \r\nevaluating auditory discrimination of tone frequency and level. The method uses \r\nsignal detection theory and a computational model for a population of auditory \r\nnerve (AN) fiber responses. Please see paper for details.", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 999, - "tag": "ModelDB:37436" - } - ], - "timestamp_created": "2024-01-11 13:52:13.133991+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/37436", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "529": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 529, - "name": "Thalamocortical augmenting response (Bazhenov et al 1998)", - "repository_type": "github", - "summary": "In the cortical model, augmenting responses were more powerful in the \"input\" layer compared with those in the \"output\" layer. Cortical stimulation of the network model produced augmenting responses in cortical neurons in distant cortical areas through corticothalamocortical loops and low-threshold intrathalamic augmentation. ... The predictions of the model were compared with in vivo recordings from neurons in cortical area 4 and thalamic ventrolateral nucleus of anesthetized cats. The known intrinsic properties of thalamic cells and thalamocortical interconnections can account for the basic properties of cortical augmenting responses. See reference for details. NEURON implementation note: cortical SU cells are getting slightly too little stimulation - reason unknown.", - "tags": [ - { - "id": 590, - "tag": "I A" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 1000, - "tag": "ModelDB:37819" - } - ], - "timestamp_created": "2024-01-11 13:52:13.630225+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/37819", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "530": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 530, - "name": "CN pyramidal fusiform cell (Kanold, Manis 2001)", - "repository_type": "github", - "summary": "Pyramidal cells in the dorsal cochlear nucleus (DCN) show three characteristic discharge patterns in response tones: pauser, buildup, and regular firing. Experimental evidence suggests that a rapidly inactivating K+ current (I(KIF)) plays a critical role in generating these discharge patterns. To explore the role of I(KIF), we used a computational model based on the biophysical data. The model replicated the dependence of the discharge pattern on the magnitude and duration of hyperpolarizing prepulses, and I(KIF) was necessary to convey this dependence. Experimentally, half-inactivation voltage and kinetics of I(KIF) show wide variability. Varying these parameters in the model ... suggests that pyramidal cells can adjust their sensitivity to different temporal patterns of inhibition and excitation by modulating the kinetics of I(KIF). Overall, I(KIF) is a critical conductance controlling the excitability of DCN pyramidal cells. (See readme.txt and paper for details).\r\n\r\nAny questions regarding these implementations should be directed to:\r\npmanis@med.unc.edu\r\n\r\n2 April 2004\r\nPaul B Manis, Ph.D.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 1001, - "tag": "ModelDB:37856" - } - ], - "timestamp_created": "2024-01-11 13:52:14.102032+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/37856", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "531": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 531, - "name": "CN bushy, stellate neurons (Rothman, Manis 2003)", - "repository_type": "github", - "summary": "Using kinetic data from three different K+ currents in acutely isolated neurons, a single electrical compartment model representing the soma of a ventral cochlear nucleus (VCN) neuron was created. The K+ currents include a fast transient current (IA), a slow-inactivating low-threshold current (ILT), and a noninactivating high-threshold current (IHT). The model also includes a fast-inactivating Na+ current, a hyperpolarization-activated cation current (Ih), and 1-50 auditory nerve synapses. With this model, the role IA, ILT, and IHT play in shaping the discharge patterns of VCN cells is explored. Simulation results indicate these currents have specific roles in shaping the firing patterns of stellate and bushy CN cells. (see readme.txt and the papers, esp 2003c, for details). Any questions regarding these implementations should be directed to: pmanis@med.unc.edu 2 April 2004 Paul B Manis, Ph.D.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 762, - "tag": "Audition" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 1002, - "tag": "ModelDB:37857" - } - ], - "timestamp_created": "2024-01-11 13:52:14.584732+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/37857", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "532": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 532, - "name": "Artificial neuron model (Izhikevich 2003, 2004, 2007)", - "repository_type": "github", - "summary": "A set of models is presented based on 2 related\r\nparameterizations to reproduce spiking and bursting behavior of multiple\r\ntypes of cortical neurons and thalamic neurons. These models combine the\r\nbiologically plausibility of Hodgkin Huxley-type dynamics and the\r\ncomputational efficiency of integrate-and-fire neurons. Using these\r\nmodel, one can simulate tens of thousands of spiking cortical neurons in\r\nreal time (1 ms resolution) using a desktop PC.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 732, - "tag": "Tutorial/Teaching" - }, - { - "id": 1003, - "tag": "ModelDB:39948" - } - ], - "timestamp_created": "2024-01-11 13:52:15.081093+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/39948", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "533": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 533, - "name": "Spiny neuron model with dopamine-induced bistability (Gruber et al 2003)", - "repository_type": "github", - "summary": "These files implement a model of dopaminergic modulation of voltage-gated currents (called kir2 and caL in the original paper). See spinycell.html for details of usage and implementation. For questions about this implementation, contact Ted Carnevale (ted.carnevale@yale.edu)", - "tags": [ - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 763, - "tag": "Intrinsic plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1004, - "tag": "ModelDB:39949" - } - ], - "timestamp_created": "2024-01-11 13:52:15.580829+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/39949", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "534": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 534, - "name": "Sparsely connected networks of spiking neurons (Brunel 2000)", - "repository_type": "github", - "summary": "The dynamics of networks of sparsely connected excitatory and inhibitory integrate-and-fire neurons\r\nare studied analytically (and with simulations). The analysis reveals a rich repertoire of states, including synchronous states in which\r\nneurons fire regularly; asynchronous states with stationary global activity and very irregular individual cell activity;\r\nand states in which the global activity oscillates but individual cells fire irregularly, typically at rates lower than\r\nthe global oscillation frequency. See paper for more and details.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 611, - "tag": "NEST" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 1005, - "tag": "ModelDB:42020" - } - ], - "timestamp_created": "2024-01-11 13:52:16.068794+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/42020", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "535": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 535, - "name": "Kenyon cells in the honeybee (Wustenberg et al 2004)", - "repository_type": "github", - "summary": "The mushroom body of the insect brain is an important locus for olfactory information\r\nprocessing and associative learning. ... Current- and voltage-clamp analyses were\r\nperformed on cultured Kenyon cells from honeybees. ... Voltage-clamp analyses characterized a\r\nfast transient Na+ current (INa), a delayed rectifier K+ current (IK,V) and a fast transient K+ current\r\n(IK,A). Using the neurosimulator SNNAP, a Hodgkin-Huxley type model was developed and\r\nused to investigate the roles of the different currents during spiking. The model led to the\r\nprediction of a slow transient outward current (IK,ST) that was subsequently identified by reevaluating\r\nthe voltage-clamp data. Simulations indicated that the primary currents that underlie\r\nspiking are INa and IK,V, whereas IK,A and IK,ST primarily determined the responsiveness of the\r\nmodel to stimuli such constant or oscillatory injections of current. See paper for more details.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 757, - "tag": "SNNAP" - }, - { - "id": 1006, - "tag": "ModelDB:42022" - } - ], - "timestamp_created": "2024-01-11 13:52:16.601453+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/42022", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "536": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 536, - "name": "Touch Sensory Cells (T Cells) of the Leech (Cataldo et al. 2004) (Scuri et al. 2007)", - "repository_type": "github", - "summary": "Bursts of spikes in leech T cells produce an AHP, which results from activation of a Na+/K+ pump and \r\na Ca2+-dependent K+ current. Activity-dependent increases in the AHP are believed to induce conduction \r\nblock of spikes in several regions of the neuron, which in turn, may decrease presynaptic invasion of spikes and \r\nthereby decrease transmitter release. To explore this possibility, we used the neurosimulator SNNAP to develop \r\na multi-compartmental model of the T cell. Each compartment was modeled as an equivalent electrical circuit, \r\nin which some currents were regulated by intracellular Ca2+ and Na+. The membrane model consisted of \r\na membrane capacitance (Cm), for which we used the value 1 uF/cm2, in parallel with \r\ntwo inward currents (Na+ and Ca2+), two K+ currents, a leak current and pump current. \r\nThe model incorporated empirical data that describe the geometry of the cell and activity-dependent changes of the\r\nAHP (see paper for details). \r\nSimulations indicated that at some branching points, activity-dependent increases of the AHP reduced the number \r\nof spikes transmitted from the minor receptive field to the soma and beyond. \r\nThese results suggest that the AHP can regulate spike conduction within the presynaptic arborizations of the cell and \r\ncould in principle contribute to the synaptic depression that is correlated with increases in the AHP.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 745, - "tag": "Conduction failure" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 740, - "tag": "Na/K pump" - }, - { - "id": 757, - "tag": "SNNAP" - }, - { - "id": 741, - "tag": "Sodium pump" - }, - { - "id": 764, - "tag": "Touch" - }, - { - "id": 1007, - "tag": "ModelDB:42036" - } - ], - "timestamp_created": "2024-01-11 13:52:17.161911+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/42036", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "537": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 537, - "name": "Effects of Acetyl-L-carnitine on neural transmission (Lombardo et al 2004)", - "repository_type": "github", - "summary": "Acetyl-L-carnitine is known to improve many aspects of the neural activity even if its exact role in neurotransmission is \r\nstill unknown. This study investigates the effects of acetyl-L-carnitine in T segmental sensory neurons of the leech Hirudo \r\nmedicinalis. These neurons are involved in some forms of neural plasticity associated with learning processes. \r\nTheir physiological firing is accompanied by a large afterhyperpolarization that is mainly due to the Na+/K+ ATPase \r\nactivity and partially to a Ca2+-dependent K+ current. A clear-cut hyperpolarization and a significant increase of the \r\nafterhyperpolarization have been recorded in T neurons of leeches injected with 2 mM acetyl-L-carnitine some days \r\nbefore. Acute treatments of 50 mM acetyl-L-carnitine induced similar effects in T cells of naive animals. \r\nMoreover, in these cells, widely arborized, the afterhyperpolarization seems to play an important role in determining \r\nthe action potential transmission at neuritic bifurcations. \r\nA computational model of a T cell has been previously developed considering detailed data for geometry and the \r\nmodulation of the pump current. Herein, we showed that to a larger afterhyperpolarization, due to the \r\nacetyl-L-carnitine-induced effects, corresponds a decrement in the number of action potentials \r\nreaching synaptic terminals.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 740, - "tag": "Na/K pump" - }, - { - "id": 757, - "tag": "SNNAP" - }, - { - "id": 741, - "tag": "Sodium pump" - }, - { - "id": 1008, - "tag": "ModelDB:42037" - } - ], - "timestamp_created": "2024-01-11 13:52:17.688514+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/42037", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "538": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 538, - "name": "Squid axon (Hodgkin, Huxley 1952) (SNNAP)", - "repository_type": "github", - "summary": "The classic HH model of squid axon membrane\r\nimplemented in SNNAP.\r\nHodgkin, A.L., Huxley, A.F. (1952)", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 757, - "tag": "SNNAP" - }, - { - "id": 1009, - "tag": "ModelDB:42044" - } - ], - "timestamp_created": "2024-01-11 13:52:18.174308+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/42044", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "539": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 539, - "name": "Morris-Lecar model of the barnacle giant muscle fiber (Morris, Lecar 1981)", - "repository_type": "github", - "summary": "... This paper presents an analysis of the possible modes of behavior available to a system of two noninactivating conductance mechanisms, and indicates a good correspondence to the types of behavior exhibited by barnacle fiber. The differential equations of a simple equivalent circuit for the fiber are dealt with by means of some of the mathematical techniques of nonlinear mechanics. General features of the system are (a) a propensity to produce damped or sustained oscillations over a rather broad parameter range, and (b) considerable latitude in the shape of the oscillatory potentials. It is concluded that for cells subject to changeable parameters (either from cell to cell or with time during cellular activity), a system dominated by two noninactivating conductances can exhibit varied oscillatory and bistable behavior. See paper for details.", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 757, - "tag": "SNNAP" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 1010, - "tag": "ModelDB:42046" - } - ], - "timestamp_created": "2024-01-11 13:52:18.711891+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/42046", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "540": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 540, - "name": "Minimal cell model (Av-Ron et al 1991)", - "repository_type": "github", - "summary": "The minimal cell model (MCM) is a reduced Hodgkin-Huxley model that can\r\nexhibit excitable and oscillatory behavior. It consists of two\r\nordinary differential equations, dV/dt for membrane voltage and dW/dt\r\nfor potassium activation and sodium inactivation. The MCM has a stable\r\nmembrane potential of -60mV. With constant input current of 10uA/cm2, it\r\nexhibits oscillations of 150Hz. It is based on the work by FitzHugh and\r\nRinzel.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 757, - "tag": "SNNAP" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 1011, - "tag": "ModelDB:42047" - } - ], - "timestamp_created": "2024-01-11 13:52:19.222590+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/42047", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "541": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 541, - "name": "Multiple modes of a conditional neural oscillator (Epstein, Marder 1990)", - "repository_type": "github", - "summary": "We present a model for a conditional bursting neuron consisting of five conductances: Hodgkin-Huxley type time- and voltage-dependent Na+ and K+ conductances, a calcium activated voltage-dependent K+ conductance, a calcium-inhibited time- and voltage-dependent Ca++ conductance, and a leakage Cl- conductance.\r\nDifferent bursting and silent modes and transitions between them are analyzed in the model and compared to bursting modes in experiment. See the paper for details.", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 765, - "tag": "I Chloride" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 757, - "tag": "SNNAP" - }, - { - "id": 1012, - "tag": "ModelDB:42321" - } - ], - "timestamp_created": "2024-01-11 13:52:19.719991+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/42321", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "542": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 542, - "name": "Spike propagation and bouton activation in terminal arborizations (Luscher, Shiner 1990)", - "repository_type": "github", - "summary": "Action potential propagation in axons with bifurcations involving short collaterals with synaptic boutons has been simulated ... The architecture of the terminal arborizations has a profound effect on the activation pattern of synapses, suggesting that terminal arborizations not only distribute neural information to postsynaptic cells but may also be able to process neural information presynaptically. Please see paper for details.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 757, - "tag": "SNNAP" - }, - { - "id": 1013, - "tag": "ModelDB:42322" - } - ], - "timestamp_created": "2024-01-11 13:52:20.209469+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/42322", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "543": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 543, - "name": "Bursting activity of neuron R15 in Aplysia (Canavier et al 1991, Butera et al 1995)", - "repository_type": "github", - "summary": "An equivalent circuit model of the R15 bursting neuron in Aplysia has been combined with a fluid compartment model, resulting in a model that incorporates descriptions of most of the membrane ion channels that are known to exist in the somata of R15, as well as providing a Ca2+ balance on the cell. ... (from the second paper) we have implemented proposed mechanisms for the modulation of two ionic currents (IR and ISI) that play key roles in regulating its spontaneous electrical activity. The model was sufficient to simulate a wide range of endogenous activity in the presence of various concentrations of 5-HT or DA. See papers for more and details.", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 757, - "tag": "SNNAP" - }, - { - "id": 1014, - "tag": "ModelDB:42323" - } - ], - "timestamp_created": "2024-01-11 13:52:20.703660+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/42323", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "544": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 544, - "name": "Olfactory bulb mitral cell: synchronization by gap junctions (Migliore et al 2005)", - "repository_type": "github", - "summary": "In a realistic model of two electrically connected mitral cells,\r\nthe paper shows that the somatically-measured experimental properties\r\nof Gap Junctions (GJs) may correspond to a variety of different local coupling strengths\r\nand dendritic distributions of GJs in the tuft. The model suggests\r\nthat the propagation of the GJ-induced local tuft depolarization\r\nis a major mechanim for intraglomerular synchronization of mitral cells.", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 1015, - "tag": "ModelDB:43039" - } - ], - "timestamp_created": "2024-01-11 13:52:21.457269+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/43039", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "545": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 545, - "name": "CA1 pyramidal neuron: dendritic spike initiation (Gasparini et al 2004)", - "repository_type": "github", - "summary": "NEURON mod files from the paper:\r\nSonia Gasparini, Michele Migliore, and Jeffrey C. Magee\r\nOn the initiation and propagation of dendritic spikes in CA1 pyramidal neurons,\r\nJ. Neurosci., J. Neurosci. 24:11046-11056 (2004).", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 595, - "tag": "Coincidence Detection" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1016, - "tag": "ModelDB:44050" - } - ], - "timestamp_created": "2024-01-11 13:52:21.970453+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/44050", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "546": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 546, - "name": "Sympathetic neuron (Wheeler et al 2004)", - "repository_type": "github", - "summary": "This study shows how synaptic convergence and plasticity can interact to generate synaptic gain in autonomic ganglia and thereby enhance homeostatic control. Using a conductance-based computational model of an idealized sympathetic neuron, we simulated the postganglionic response to noisy patterns of presynaptic activity and found that a threefold amplification in postsynaptic spike output can arise in ganglia, depending on the number and strength of nicotinic synapses, the presynaptic firing rate, the extent of presynaptic facilitation, and the expression of muscarinic and peptidergic excitation. See references for details.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 766, - "tag": "I CNG" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 768, - "tag": "Synaptic Convergence" - }, - { - "id": 1017, - "tag": "ModelDB:44972" - } - ], - "timestamp_created": "2024-01-11 13:52:22.474516+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/44972", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "547": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 547, - "name": "The activity phase of postsynaptic neurons (Bose et al 2004)", - "repository_type": "github", - "summary": "We show, in a simplified network consisting of an oscillator \r\ninhibiting a follower neuron, how the interaction between synaptic depression\r\nand a transient potassium current in the follower neuron determines the \r\nactivity phase of this neuron. We derive a mathematical expression to \r\ndetermine at what phase of the oscillation the follower neuron becomes \r\nactive. This expression can be used to understand which parameters determine\r\nthe phase of activity of the follower as the frequency of the oscillator is \r\nchanged. See paper for more.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1018, - "tag": "ModelDB:45513" - } - ], - "timestamp_created": "2024-01-11 13:52:22.957206+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/45513", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "548": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 548, - "name": "Large cortex model with map-based neurons (Rulkov et al 2004)", - "repository_type": "github", - "summary": "We develop a new computationally efficient approach for the analysis of complex large-scale neurobiological networks. Its key element is the use of a new phenomenological model of a neuron capable of replicating important spike pattern characteristics and designed in the form of a system of difference equations (a map). ... Interconnected with synaptic currents these model neurons demonstrated responses very similar to those found with Hodgkin-Huxley models and in experiments. We illustrate the efficacy of this approach in simulations of one- and two-dimensional cortical network models consisting of regular spiking neurons and fast spiking interneurons to model sleep and activated states of the thalamocortical system. See paper for more.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 592, - "tag": "Sleep" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 1019, - "tag": "ModelDB:45525" - } - ], - "timestamp_created": "2024-01-11 13:52:23.462327+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/45525", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "549": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 549, - "name": "A single column thalamocortical network model (Traub et al 2005)", - "repository_type": "github", - "summary": "To better understand population phenomena in thalamocortical neuronal ensembles,\r\nwe have constructed a preliminary network model with 3,560 multicompartment neurons\r\n(containing soma, branching dendrites, and a portion of axon). Types of neurons included\r\nsuperficial pyramids (with regular spiking [RS] and fast rhythmic bursting [FRB] firing\r\nbehaviors); RS spiny stellates; fast spiking (FS) interneurons, with basket-type and axoaxonic\r\ntypes of connectivity, and located in superficial and deep cortical layers; low threshold spiking\r\n(LTS) interneurons, that contacted principal cell dendrites; deep pyramids, that could have RS or\r\nintrinsic bursting (IB) firing behaviors, and endowed either with non-tufted apical dendrites or\r\nwith long tufted apical dendrites; thalamocortical relay (TCR) cells; and nucleus reticularis\r\n(nRT) cells. To the extent possible, both electrophysiology and synaptic connectivity were\r\nbased on published data, although many arbitrary choices were necessary.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 710, - "tag": "FORTRAN" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 592, - "tag": "Sleep" - }, - { - "id": 593, - "tag": "Spindles" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 1020, - "tag": "ModelDB:45539" - } - ], - "timestamp_created": "2024-01-11 13:52:24.073934+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/45539", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "550": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 550, - "name": "Bursting and resonance in cerebellar granule cells (D'Angelo et al. 2001)", - "repository_type": "github", - "summary": "In this study we report theta-frequency (3-12 Hz)\r\nbursting and resonance in rat cerebellar granule cells and show that these neurons express a previously unidentified slow repolarizing K1 current (IK-slow ). Our experimental and modeling results indicate that IK-slow was necessary for both bursting and resonance. See paper for more.", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 1021, - "tag": "ModelDB:46839" - } - ], - "timestamp_created": "2024-01-11 13:52:24.576307+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/46839", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "551": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 551, - "name": "Cerebellar Purkinje Cell: resurgent Na current and high frequency firing (Khaliq et al 2003)", - "repository_type": "github", - "summary": "These mod files supplied by Dr Raman are for the below two references. ... we modeled action potential firing by simulating eight currents directly recorded from Purkinje cells in both wild-type and (mutant) med mice.\r\nRegular, high-frequency firing was slowed in med Purkinje neurons. In addition to disrupted sodium currents, med neurons had small\r\nbut significant changes in potassium and leak currents. Simulations indicated that these modified non-sodium currents could not\r\naccount for the reduced excitability of med cells but instead slightly facilitated spiking. The loss of NaV1.6-specific kinetics, however,\r\nslowed simulated spontaneous activity. Together, the data suggest that across a range of conditions, sodium currents with a resurgent\r\ncomponent promote and accelerate firing. See papers for more and details.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1022, - "tag": "ModelDB:48332" - } - ], - "timestamp_created": "2024-01-11 13:52:25.082227+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/48332", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "552": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 552, - "name": "Pyramidal neurons: IKHT offsets activation of IKLT to increase gain (Fernandez et al 2005)", - "repository_type": "github", - "summary": "This matlab model was supplied by Dr Fernandez. It provides the model specification for the below paper. The influence of a high threshold K current on low threshold K and Na currents (especially frequency-current relationships) are studied in the paper with both experiments and modeling. Please see the reference for more and details.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 769, - "tag": "I_KHT" - }, - { - "id": 770, - "tag": "I_KLT" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1023, - "tag": "ModelDB:48506" - } - ], - "timestamp_created": "2024-01-11 13:52:25.570040+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/48506", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "553": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 553, - "name": "Spike timing detection in different forms of LTD (Doi et al 2005)", - "repository_type": "github", - "summary": "To understand the spike-timing detection mechanisms in cerebellar long-term depression (LTD), we developed a kinetic model of Ca dynamics within a Purkinje dendritic spine. In our kinetic simulation, IP3 was first produced via the metabotropic pathway of parallel fiber (PF) inputs, and the Ca influx in response to the climbing fiber (CF) input triggered regenerative Ca-induced Ca release from the internal stores via the IP3\r\nreceptors activated by the increased IP3. The delay in IP3 increase caused by the PF metabotropic pathway generated the optimal PF\u2013CF\r\ninterval. The Ca dynamics revealed a threshold for large Ca2 release that decreased as IP3 increased, and it coherently explained the\r\ndifferent forms of LTD. See paper for more and details.", - "tags": [ - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 595, - "tag": "Coincidence Detection" - }, - { - "id": 722, - "tag": "Depression" - }, - { - "id": 750, - "tag": "GENESIS (web link to model)" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 751, - "tag": "Signaling pathways" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 1024, - "tag": "ModelDB:49305" - } - ], - "timestamp_created": "2024-01-11 13:52:26.085058+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/49305", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "554": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 554, - "name": "Kinetic NMDA receptor model (Kampa et al 2004)", - "repository_type": "github", - "summary": "This kinetic NMDA receptor model is based on voltage-clamp recordings of NMDA receptor-mediated currents in nucleated patches of rat neocortical layer 5 pyramidal neurons (Kampa et al 2004 J Physiol), this model was fit with AxoGraph directly to experimental recordings in order to obtain the optimal values for the parameters. The demo shows the behaviour of a kinetic NMDA receptor model reproducing the data in figure 2.\r\nThe NMDA receptor model uses realistic rates of magnesium block and its effects on channel desensitisation. Presynaptic transmitter release is necessary for glutamate binding to the receptor. This model was written by Bjoern Kampa, Canberra, 2004.", - "tags": [ - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 1025, - "tag": "ModelDB:50207" - } - ], - "timestamp_created": "2024-01-11 13:52:26.594001+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/50207", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "555": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 555, - "name": "Olfactory bulb granule cell: effects of odor deprivation (Saghatelyan et al 2005)", - "repository_type": "github", - "summary": "The model supports the experimental findings on the effects of postnatal odor deprivation, and shows that a -10mV shift in the\r\nNa activation or a reduction in the dendritic length of newborn GC\r\ncould independently explain the observed increase in excitability. \r\n", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 1026, - "tag": "ModelDB:50210" - } - ], - "timestamp_created": "2024-01-11 13:52:27.084354+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/50210", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "556": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 556, - "name": "Spontaneous firing caused by stochastic channel gating (Chow, White 1996)", - "repository_type": "github", - "summary": "NEURON implementation of model of stochastic channel gating, resulting in spontaneous firing. Qualitatively reproduces the phenomena described in \r\nthe reference.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 1027, - "tag": "ModelDB:50391" - } - ], - "timestamp_created": "2024-01-11 13:52:27.647805+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/50391", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "557": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 557, - "name": "Fast oscillations in inhibitory networks (Maex, De Schutter 2003)", - "repository_type": "github", - "summary": "We observed a new phenomenon of resonant synchronization in computer-simulated networks of inhibitory neurons in which the synaptic current has a delayed onset, reflecting finite spike propagation and synaptic transmission times. At the resonant level of network excitation, all neurons fire synchronously and rhythmically with a period approximately four times the mean delay of the onset of the inhibitory synaptic current. ... By varying the axonal delay of the inhibitory connections, networks with a realistic synaptic kinetics can be tuned to frequencies from 40 to >200 Hz. ... We conclude that the delay of the synaptic current is the primary parameter controlling the oscillation frequency of inhibitory networks and propose that delay-induced synchronization is a mechanism for fast brain rhythms that depend on intact inhibitory synaptic transmission.", - "tags": [ - { - "id": 750, - "tag": "GENESIS (web link to model)" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 1028, - "tag": "ModelDB:50392" - } - ], - "timestamp_created": "2024-01-11 13:52:28.246718+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/50392", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "558": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 558, - "name": "Neural model of two-interval discrimination (Machens et al 2005)", - "repository_type": "github", - "summary": "Two-interval discrimination involves comparison of two stimuli that are presented at different times. It has three phases: loading, in which the first stimulus is perceived and stored in working memory; maintenance of working memory; decision making, in which the second stimulus is perceived and compared with the first. In behaving monkeys, each phase is associated with characteristic firing activity of neurons in the prefrontal cortex. This model implements both working memory and decision making with a mutual inhibition network that reproduces all three phases of two-interval discrimination.\r\nMachens, C.K., Romo, R., and Brody, C.D.\r\nFlexible control of mutual inhibition: a neural model of two-interval discrimination.\r\nScience 307:1121-1124, 2005.", - "tags": [ - { - "id": 771, - "tag": "Action Selection/Decision Making" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 772, - "tag": "Maintenance" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1029, - "tag": "ModelDB:50656" - } - ], - "timestamp_created": "2024-01-11 13:52:28.859901+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/50656", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "559": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 559, - "name": "Ribbon Synapse (Sikora et al 2005)", - "repository_type": "github", - "summary": "A model of the ribbon synapse was developed to replicate both pre- and postsynaptic functions of this glutamatergic juncture. The presynaptic portion of the model is rich in anatomical and physiological detail and includes multiple release sites for each ribbon based on anatomical studies of presynaptic terminals, presynaptic voltage at the terminal, the activation of voltage-gated calcium channels and a calcium-dependent release mechanism whose rate varies as a function of the calcium concentration that is monitored at two different sites which control both an ultrafast, docked pool of vesicles and a release ready pool of tethered vesicles. See paper for more and details.", - "tags": [ - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 763, - "tag": "Intrinsic plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1030, - "tag": "ModelDB:50997" - } - ], - "timestamp_created": "2024-01-11 13:52:29.357282+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/50997", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "560": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 560, - "name": "Dorsal root ganglion (DRG) neuronal model (Amir, Devor 2003)", - "repository_type": "github", - "summary": "The model shows that an electrically excitable soma is not necessary for spike through-conduction in the t-shaped geometry of a dorsal root ganglion neuron axon. Electrical excitability of the soma is required, however, for soma spike invasion. See papers for details and more.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1031, - "tag": "ModelDB:51022" - } - ], - "timestamp_created": "2024-01-11 13:52:29.851928+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/51022", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "561": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 561, - "name": "LTP in cerebellar mossy fiber-granule cell synapses (Saftenku 2002)", - "repository_type": "github", - "summary": "We simulated synaptic transmission and modified a simple model of long-term potentiation (LTP) and long-term depression (LTD) in order to describe long-term plasticity related changes in cerebellar mossy fiber-granule cell synapses. In our model, protein autophosphorylation, leading to the maintenance of long-term plasticity, is controlled by Ca2+ entry through the NMDA receptor channels. The observed nonlinearity in the development of long-term changes of EPSP in granule cells is explained by the difference in the rate constants of two independent autocatalytic processes.\r\n", - "tags": [ - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 772, - "tag": "Maintenance" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 1032, - "tag": "ModelDB:51196" - } - ], - "timestamp_created": "2024-01-11 13:52:30.538387+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/51196", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "562": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 562, - "name": "Dentate gyrus network model (Santhakumar et al 2005)", - "repository_type": "github", - "summary": "Mossy cell loss and mossy fiber sprouting are two characteristic \r\n\r\nconsequences of repeated seizures and head trauma. However, their \r\n\r\nprecise contributions to the hyperexcitable state are not well \r\n\r\nunderstood. Because it is difficult, and frequently impossible, to \r\n\r\nindependently examine using experimental techniques whether it is the\r\n\r\nloss of mossy cells or the sprouting of mossy fibers that leads to \r\n\r\ndentate hyperexcitability, we built a biophysically realistic and \r\n\r\nanatomically representative computational model of the dentate gyrus\r\n\r\nto examine this question. The 527-cell model, containing granule, \r\n\r\nmossy, basket, and hilar cells with axonal projections to the \r\n\r\nperforant-path termination zone, showed that even weak mossy fiber \r\n\r\nsprouting (10-15% of the strong sprouting observed in the pilocarpine\r\n\r\nmodel of epilepsy) resulted in the spread of seizure-like activity to\r\n\r\nthe adjacent model hippocampal laminae after focal stimulation of the\r\n\r\nperforant path. See reference for more and details.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 773, - "tag": "neuroConstruct (web link to model)" - }, - { - "id": 1033, - "tag": "ModelDB:51781" - } - ], - "timestamp_created": "2024-01-11 13:52:31.082059+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/51781", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "563": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 563, - "name": "Cortical network model of posttraumatic epileptogenesis (Bush et al 1999)", - "repository_type": "github", - "summary": "This simulation from Bush, Prince, and Miller 1999 shows the epileptiform response (Fig. 6C) to a brief single stimulation in a 500 cell\r\nnetwork of multicompartment models, some of which have active dendrites. The results which I obtained under Redhat Linux is shown in result.gif.\r\n\r\nOriginal 1997 code from Paul Bush modified slightly by Bill Lytton to make it work with\r\ncurrent version of NEURON (5.7.139). Thanks to Paul Bush and Ken Miller for\r\nmaking the code available.\r\n", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 1034, - "tag": "ModelDB:52034" - } - ], - "timestamp_created": "2024-01-11 13:52:31.629225+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/52034", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "564": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 564, - "name": "Motoneuron simulations for counting motor units (Major and Jones 2005)", - "repository_type": "github", - "summary": "Simulations of clinical methods to count the number of motoneurons/motor units in human patients. Models include stimulation of motor axons or voluntary activation and responses are measured as muscle tension or EMG.", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 1035, - "tag": "ModelDB:53425" - } - ], - "timestamp_created": "2024-01-11 13:52:32.108878+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/53425", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "565": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 565, - "name": "Paired turbulence and light effect on calcium increase in Hermissenda (Blackwell 2004)", - "repository_type": "github", - "summary": "The sea slug Hermissenda learns to associate light and hair cell stimulation, but not when the stimuli are temporally uncorrelated...These issues were addressed using a multi-compartmental computer model of phototransduction, calcium dynamics, and ionic currents of the Hermissenda photoreceptor...simulations show that a potassium leak channel, which closes with an increase in calcium, is required to produce both the untrained LLD and the enhanced LLD due to the decrease in voltage dependent potassium currents.", - "tags": [ - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 774, - "tag": "Chemesis" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 751, - "tag": "Signaling pathways" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 1036, - "tag": "ModelDB:53427" - } - ], - "timestamp_created": "2024-01-11 13:52:32.579173+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/53427", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "566": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 566, - "name": "Compartmental model of a mitral cell (Popovic et al. 2005)", - "repository_type": "github", - "summary": "Usage of a morphologically realistic compartmental model of a mitral cell and data obtained from whole-cell patch-clamp and voltage-imaging experiments in order to explore passive parameter space in which reported low EPSP attenuation is observed.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 1037, - "tag": "ModelDB:53435" - } - ], - "timestamp_created": "2024-01-11 13:52:33.097179+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/53435", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "567": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 567, - "name": "Discrete event simulation in the NEURON environment (Hines and Carnevale 2004)", - "repository_type": "github", - "summary": "A short introduction to how \"integrate and fire\" cells are implemented in NEURON. Network simulations that use only artificial spiking cells are extremely efficient, with runtimes proportional to the total number of synaptic inputs received and independent of the number of cells or problem time.", - "tags": [ - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 732, - "tag": "Tutorial/Teaching" - }, - { - "id": 1038, - "tag": "ModelDB:53437" - } - ], - "timestamp_created": "2024-01-11 13:52:33.585022+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/53437", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "568": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 568, - "name": "Spatial gridding and temporal accuracy in NEURON (Hines and Carnevale 2001)", - "repository_type": "github", - "summary": "A heuristic for compartmentalization based on\r\nthe space constant at 100 Hz is proposed.\r\nThe paper also discusses spatio/temporal accuracy\r\nand the use of CVODE.", - "tags": [ - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 732, - "tag": "Tutorial/Teaching" - }, - { - "id": 1039, - "tag": "ModelDB:53451" - } - ], - "timestamp_created": "2024-01-11 13:52:34.059050+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/53451", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "569": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 569, - "name": "Method of probabilistic principle surfaces (PPS) (Chang and Ghosh 2001)", - "repository_type": "github", - "summary": "Principal curves and surfaces are nonlinear generalizations of principal\r\ncomponents and subspaces, respectively. They can provide insightful summary\r\nof high-dimensional data not typically attainable by classical linear\r\nmethods. See paper for more and details. The matlab code supplied at the authors website calculates probabilistic principle surfaces on benchmark data sets.", - "tags": [ - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1040, - "tag": "ModelDB:53457" - } - ], - "timestamp_created": "2024-01-11 13:52:34.517350+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/53457", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "570": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 570, - "name": "S cell network (Moss et al 2005)", - "repository_type": "github", - "summary": "Excerpts from the abstract:\r\nS cells form a chain of electrically coupled neurons that extends the length of the leech CNS and plays a critical role in sensitization during whole-body shortening. ...\r\nSerotonin ... increasedAP latency across the electrical synapse, suggesting that serotonin reduced coupling between S cells. ...\r\nSerotonin modulated \r\ninstantaneous AP frequency when APs were initiated in separate S \r\ncells and in a computational model of S cell activity following \r\nmechanosensory input. Thus, serotonergic modulation of S cell \r\nelectrical synapses may contribute to changes in the pattern of\r\nactivity in the S cell network. See paper for more.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 757, - "tag": "SNNAP" - }, - { - "id": 1041, - "tag": "ModelDB:53559" - } - ], - "timestamp_created": "2024-01-11 13:52:34.992390+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/53559", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "571": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 571, - "name": "Tonic neuron in spinal lamina I: prolongation of subthreshold depol. (Prescott and De Koninck 2005)", - "repository_type": "github", - "summary": "Model demonstrates mechanism whereby two kinetically distinct inward currents act synergistically to prolong subthreshold depolarization. The important currents are a persistent Na current (with fast kinetics) and a persistent Ca current (with slower kinetics). Model also includes a slow K current and transient Ca current, in addition to standard HH currents. Model parameters are set to values used in Fig. 8A. Simulation shows prolonged depolarizations in response to two brief stimuli.\r\n", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 754, - "tag": "Delay" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 775, - "tag": "Nociception" - }, - { - "id": 1042, - "tag": "ModelDB:53569" - } - ], - "timestamp_created": "2024-01-11 13:52:35.477471+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/53569", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "572": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 572, - "name": "Rat phrenic motor neuron (Amini et al 2004)", - "repository_type": "github", - "summary": "We have developed a model for the rat phrenic motor neuron (PMN) that robustly replicates many experimentally observed behaviors of PMNs in response to pharmacological, ionic, and electrical perturbations using a single set of parameters.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 776, - "tag": "C or Cplusplus program (web link to model)" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 777, - "tag": "Spike Frequency Adaptation" - }, - { - "id": 1043, - "tag": "ModelDB:53572" - } - ], - "timestamp_created": "2024-01-11 13:52:35.964480+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/53572", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "573": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 573, - "name": "A model for interaural time difference sensitivity in the medial superior olive (Zhou et al 2005)", - "repository_type": "github", - "summary": "This model simulates responses of neurons to interaural time difference (ITD) in the medial superior olive (MSO) of the mammalian brainstem. The model has a bipolar cell structure and incorporates two anatomic observations in the MSO: (1) the axon arises from the dendrite that receives ipsilateral inputs and (2) inhibitory synapses are located primarily on the soma in adult animals. Fine adjustment of the best ITD is achieved by the interplay of somatic sodium currents and synaptic inhibitory currents. The model suggests a mechanism for dynamically \"fine-tuning\" the ITD sensitivity of MSO cells by the opponency between depolarizing sodium currents and hyperpolarizing inhibitory currents.", - "tags": [ - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 595, - "tag": "Coincidence Detection" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1044, - "tag": "ModelDB:53869" - } - ], - "timestamp_created": "2024-01-11 13:52:36.480839+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/53869", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "574": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 574, - "name": "Medial vestibular neuron models (Quadroni and Knopfel 1994)", - "repository_type": "github", - "summary": "The structure and the parameters of the model cells were chosen to reproduce the responses of type A and type B MVNns as described in electrophysiological recordings. The emergence of oscillatory firing under these two specific experimental conditions is consistent with electrophysiological recordings not used during construction of the model. We, therefore, suggest that these models have a high predictive value.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 1045, - "tag": "ModelDB:53876" - } - ], - "timestamp_created": "2024-01-11 13:52:36.976861+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/53876", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "575": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 575, - "name": "T-type Ca current in thalamic neurons (Wang et al 1991)", - "repository_type": "github", - "summary": "A model of the transient, low-threshold voltage-dependent (T-type)\r\nCa2+ current is constructed using whole-cell voltage-clamp \r\ndata from enzymatically isolated rat thalamocortical relay neurons. \r\nThe T-type Ca2+ current is described according to the Hodgkin-Huxley \r\nscheme, using the m3h format, with rate constants determined from the experimental data.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 1046, - "tag": "ModelDB:53893" - } - ], - "timestamp_created": "2024-01-11 13:52:37.476012+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/53893", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "576": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 576, - "name": "C elegans pharynx simulation (Avery and Shtonda 2003)", - "repository_type": "github", - "summary": "Experimental obervations, measurements, and theoretical analysis of C. elegans pharynx feeding behavior function are reported in the paper. See the paper and the model files for more.", - "tags": [ - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 778, - "tag": "Java" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1047, - "tag": "ModelDB:53894" - } - ], - "timestamp_created": "2024-01-11 13:52:37.983503+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/53894", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "577": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 577, - "name": "T channel currents (Vitko et al 2005)", - "repository_type": "github", - "summary": "Computer simulations predict that seven of the SNPs would increase firing of neurons, with three of them inducing oscillations at similar frequencises. 3 representative models from the paper have been submited: a wild-type (WT) recombinant Cav3.2 T-channel, and two of the\r\nmutants described in the Vitko et al., 2005 paper (C456S and R788C). See the paper for more and details.\r\n", - "tags": [ - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1048, - "tag": "ModelDB:53965" - } - ], - "timestamp_created": "2024-01-11 13:52:38.484489+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/53965", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "578": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 578, - "name": "NEUROFIT: fitting HH models to voltage clamp data (Willms 2002)", - "repository_type": "github", - "summary": "Publicly available software for accurate fitting of Hodgkin-Huxley models to voltage-clamp data... The set of parameter values for the model determined by this software yield current traces that are substantially closer to the observed data than those determined from the usual fitting method. This improvement is due to the fact that the software fits all of the parameters simultaneously utilizing all of the data rather than fitting steady-state and time constant parameters disjointly using peak currents and portions of the rising and falling phases... The software also incorporates a linear pre-estimation procedure to help in determining reasonable initial values for the full non-linear algorithm. See references for details and more.", - "tags": [ - { - "id": 776, - "tag": "C or Cplusplus program (web link to model)" - }, - { - "id": 710, - "tag": "FORTRAN" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 1049, - "tag": "ModelDB:54141" - } - ], - "timestamp_created": "2024-01-11 13:52:38.970393+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/54141", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "579": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 579, - "name": "Channel parameter estimation from current clamp and neuronal properties (Toth, Crunelli 2001)", - "repository_type": "github", - "summary": "In this paper, we present a method by which the activation and kinetic properties of INa, IK can be estimated from current-clamp data, more precisely from the time course of the action potential, provided some additional electrophysiological properties of the neurone are a priori known. See reference for details and more.", - "tags": [ - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 779, - "tag": "Octave" - }, - { - "id": 1050, - "tag": "ModelDB:54154" - } - ], - "timestamp_created": "2024-01-11 13:52:39.464749+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/54154", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "580": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 580, - "name": "Olfactory receptor neuron model (Dougherty et al 2005)", - "repository_type": "github", - "summary": "Demonstration of ORN model by Dougherty, Wright and Yew (2005) PNAS 102: 10415-10420. This program, dwy_pnas_demo2, simulates the transduction current response of a single olfactory receptor neuron being stimulated by an odorant plume. The program is interactive in that a user can tweak parameter values and stimulus conditions. Also, users can save a configuration in a mat-file or export all aspects to a directory of text files. These text files can be read by other programs. There is also an import facility for importing text files from a directory that allows the user to specify their own data, pulses and parameters.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 766, - "tag": "I CNG" - }, - { - "id": 780, - "tag": "I Cl,Ca" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 1051, - "tag": "ModelDB:54896" - } - ], - "timestamp_created": "2024-01-11 13:52:39.962636+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/54896", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "581": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 581, - "name": "AP initiation and propagation in type II cochlear ganglion cell (Hossain et al 2005)", - "repository_type": "github", - "summary": "The model of type II cochlear ganglion cell was based on the\r\nimmunostaining of the mouse auditory pathway. Specific antibodies were\r\nused to map the distribution of voltage-dependent sodium channels along\r\nthe two unmyelinated axon-like processes of the bipolar ganglion cells.\r\nThree distinct hot spots were detected. A high density of sodium\r\nchannels was present over the entire trajectory of sensory endings\r\nbeneath the outer hair cells (the most distal portion of the peripheral\r\naxon). The other two hot spots were localized in the initial segments of\r\nboth of the axons that flank the unmyelinated bipolar ganglion cell bodies.\r\n\r\nA biophysical model indicates that all three hot spots might play\r\nimportant roles in action potential initiation and propagation. For\r\ninstance, the hot spot in the receptor segment is important for\r\ntransforming the receptor potentials into a full blown action potential\r\n(Supplemental Fig. 1). The hot spots in the two paraganglionic axon\r\ninitial segments are there to ensure the successful propagation of\r\naction potentials from the peripheral to the central axon through the\r\ncell body.\r\n\r\nThe Readme.txt file provides step by step instructions on how to\r\nrecreate Figures 6 and 7 of Hossain et al., 2005 paper.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 762, - "tag": "Audition" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1052, - "tag": "ModelDB:54903" - } - ], - "timestamp_created": "2024-01-11 13:52:40.571287+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/54903", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "582": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 582, - "name": "CA1 pyramidal neuron: signal propagation in oblique dendrites (Migliore et al 2005)", - "repository_type": "github", - "summary": "NEURON mod files from the paper:\r\nM. Migliore, M. Ferrante, GA Ascoli (2005).\r\nThe model shows how the back- and forward propagation of action potentials in the oblique dendrites of CA1 neurons could be modulated by local properties such as morphology or active conductances.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 773, - "tag": "neuroConstruct (web link to model)" - }, - { - "id": 1053, - "tag": "ModelDB:55035" - } - ], - "timestamp_created": "2024-01-11 13:52:41.048146+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/55035", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "583": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 583, - "name": "Regulation of KCNQ2/KCNQ3 current by G protein cycling (Suh et al 2004)", - "repository_type": "github", - "summary": "Receptor-mediated modulation of KCNQ channels regulates neuronal excitability. This study concerns\r\nthe kinetics and mechanism of M1 muscarinic receptor-mediated regulation of the cloned neuronal M channel, KCNQ2/KCNQ3 (Kv7.2/Kv7.3). ... observations were successfully described by a kinetic model representing biochemical steps of the signaling\r\ncascade using published rate constants where available. The model supports the following sequence of events for\r\nthis Gq-coupled signaling: A classical G-protein cycle, including competition for nucleotide-free G-protein by all\r\nnucleotide forms and an activation step requiring Mg2, followed by G-protein-stimulated phospholipase C and\r\nhydrolysis of PIP2, and finally PIP2 dissociation from binding sites for inositol lipid on the channels so that KCNQ\r\ncurrent was suppressed. See paper for details and more.", - "tags": [ - { - "id": 781, - "tag": "G-protein coupled" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 751, - "tag": "Signaling pathways" - }, - { - "id": 782, - "tag": "Virtual Cell (web link to model)" - }, - { - "id": 1054, - "tag": "ModelDB:55273" - } - ], - "timestamp_created": "2024-01-11 13:52:41.526746+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/55273", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "584": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 584, - "name": "Role of KCNQ1 and IKs in cardiac repolarization (Silva, Rudy 2005)", - "repository_type": "github", - "summary": "Detailed Markov models of IKs (the slow delayed rectifier K+ current) and its alpha-subunit KCNQ1 were developed. The model is compared to experiment in the paper. The role of IKs in disease and drug treatments is elucidated (the prevention of excessive action potential prolongation and development of arrhythmogenic early afterdepolarizations). See paper for more and details.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 737, - "tag": "Heart disease" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 783, - "tag": "I_Ks" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 784, - "tag": "KCNQ1" - }, - { - "id": 785, - "tag": "Long-QT" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 748, - "tag": "Markov-type model" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1055, - "tag": "ModelDB:55748" - } - ], - "timestamp_created": "2024-01-11 13:52:42.005907+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/55748", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "585": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 585, - "name": "Breakdown of accmmodation in nerve: a possible role for INAp (Hennings et al 2005)", - "repository_type": "github", - "summary": "The present modeling study suggests that persistent, low-threshold, rapidly activating\r\nsodium currents have a key role in breakdown of accommodation, and that breakdown of\r\naccommodation can be used as a tool for studying persistent sodium current under normal and\r\npathological conditions. See paper for more and details.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 786, - "tag": "Electrotonus" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 787, - "tag": "Pathophysiology" - }, - { - "id": 1056, - "tag": "ModelDB:55749" - } - ], - "timestamp_created": "2024-01-11 13:52:42.494664+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/55749", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "586": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 586, - "name": "A dynamic model of the canine ventricular myocyte (Hund, Rudy 2004)", - "repository_type": "github", - "summary": "The Hund-Rudy dynamic (HRd) model is based on data from the canine epicardial ventricular myocyte. Rate-dependent phenomena associated with ion channel kinetics, action potential properties and Ca2+ handling are simulated by the model. See paper for more and details.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 776, - "tag": "C or Cplusplus program (web link to model)" - }, - { - "id": 737, - "tag": "Heart disease" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 765, - "tag": "I Chloride" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 740, - "tag": "Na/K pump" - }, - { - "id": 741, - "tag": "Sodium pump" - }, - { - "id": 1057, - "tag": "ModelDB:55756" - } - ], - "timestamp_created": "2024-01-11 13:52:42.977666+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/55756", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "587": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 587, - "name": "Ventricular cell model (Guinea-pig-type) (Luo, Rudy 1991, +11 other papers!) (C++)", - "repository_type": "github", - "summary": "A mathematical model of the membrane action potential of the mammalian ventricular cell is introduced. The model is based, whenever possible, on recent single-cell and single-channel data and incorporates the possibility of changing extracellular potassium concentration [K]o. ... The results are consistent with recent experimental observations, and the model simulations relate these phenomena to the underlying ionic channel kinetics. See paper for more and details.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 776, - "tag": "C or Cplusplus program (web link to model)" - }, - { - "id": 737, - "tag": "Heart disease" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 783, - "tag": "I_Ks" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1058, - "tag": "ModelDB:55859" - } - ], - "timestamp_created": "2024-01-11 13:52:43.493930+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/55859", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "588": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 588, - "name": "Boundary effects influence velocity in transverse propagation of cardiac APs (Sperelakis et al 2005)", - "repository_type": "github", - "summary": "... earlier experiments were carried out with 2-dimensional\r\nsheets of cells: 2 \u00c3\u2014 3, 3 \u00c3\u2014 4, and 5 \u00c3\u2014 5 models (where the first number is the number of parallel\r\nchains and the second is the number of cells in each chain). The purpose of the present study was\r\nto enlarge the model size to 7 \u00c3\u2014 7, thus enabling the transverse velocities to be compared in models\r\nof different sizes (where all circuit parameters are identical in all models). This procedure should\r\nenable the significance of the role of edge (boundary) effects in transverse propagation to be\r\ndetermined. See paper for more and details.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 788, - "tag": "PSpice" - }, - { - "id": 1059, - "tag": "ModelDB:56012" - } - ], - "timestamp_created": "2024-01-11 13:52:43.989974+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/56012", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "589": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 589, - "name": "Persistent synchronized bursting activity in cortical tissues (Golomb et al 2005)", - "repository_type": "github", - "summary": "The program simulates a one-dimensional model of a cortical tissue with excitatory and inhibitory populations.\r\n", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 1060, - "tag": "ModelDB:57905" - } - ], - "timestamp_created": "2024-01-11 13:52:44.484760+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/57905", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "590": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 590, - "name": "HERG K+ channels spike-frequency adaptation (Chiesa et al 1997)", - "repository_type": "github", - "summary": "Spike frequency adaptation has contributions from the IHERG current (encoded by the human eag-related gene\r\n(HERG); Warmke & Ganetzky, 1994), which develops with\r\nslow kinetics during depolarization and contributes to the\r\nrepolarization of the long action potentials typically present\r\nin the heart. IHERG is one of the delayed rectifier currents\r\n(IK(r)) of the heart, and HERG mutations are associated\r\nwith one of the cardiac arrhythmia LQT syndromes (LQT2).\r\nSee paper for more and details.\r\n", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 789, - "tag": "I_HERG" - }, - { - "id": 785, - "tag": "Long-QT" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 777, - "tag": "Spike Frequency Adaptation" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1061, - "tag": "ModelDB:57910" - } - ], - "timestamp_created": "2024-01-11 13:52:44.971994+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/57910", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "591": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 591, - "name": "Consequences of HERG mutations in the long QT syndrome (Clancy, Rudy 2001)", - "repository_type": "github", - "summary": "This study demonstrates which mutations can prolong APD sufficiently to generate early \r\nafterdepolarizations (EADs), which may trigger life-threatening arrhythmias. The severity of the phenotype is shown to \r\ndepend on the specific kinetic changes and how they affect I(Kr) during the time course of the action potential. See paper for more and details. \r\n\r\n", - "tags": [ - { - "id": 737, - "tag": "Heart disease" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 789, - "tag": "I_HERG" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 785, - "tag": "Long-QT" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1062, - "tag": "ModelDB:58172" - } - ], - "timestamp_created": "2024-01-11 13:52:45.492560+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/58172", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "592": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 592, - "name": "Allosteric gating of K channels (Horrigan et al 1999)", - "repository_type": "github", - "summary": "Calcium sensitive large-conductance K channel conductance is controlled by both cytoplasmic calcium and membrane potential.\r\nExperimental data obtained by the inside out patch method can be understood in terms of a gating scheme where a central transition between a closed and an open conformation is allosterically regulated by the state of four independent and identical voltage sensors. See paper for more and details.", - "tags": [ - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1063, - "tag": "ModelDB:58195" - } - ], - "timestamp_created": "2024-01-11 13:52:45.961806+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/58195", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "593": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 593, - "name": "Ca(2+) oscillations based on Ca-induced Ca-release (Dupont et al 1991)", - "repository_type": "github", - "summary": "We consider a simple, minimal model for signal-induced Ca2+ oscillations based on Ca(2+)-induced Ca2+ release. The model takes into account the existence of two pools of intracellular Ca2+, namely, one sensitive to inositol 1,4,5 trisphosphate (InsP3) whose synthesis is elicited by the stimulus, and one insensitive to InsP3. See paper for more and details.", - "tags": [ - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 790, - "tag": "Calcium waves" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 751, - "tag": "Signaling pathways" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1064, - "tag": "ModelDB:58199" - } - ], - "timestamp_created": "2024-01-11 13:52:46.426060+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/58199", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "594": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 594, - "name": "Role of KCNQ1 and IKs in cardiac repolarization (Silva, Rudy 2005) (XPP)", - "repository_type": "github", - "summary": "Detailed Markov model of IKs (the slow delayed rectifier K+ current) is supplied here in XPP. The model is compared to experiment in the paper. The role of IKs in disease and drug treatments is elucidated (the prevention of excessive action potential prolongation and development of arrhythmogenic early afterdepolarizations). See also modeldb accession number 55748 code and reference for more and details. This XPP version of the model reproduces Figure 3C in the paper by default.\r\nThese model files were submitted by: Dr. Sheng-Nan Wu, Han-Dong Chang, Jiun-Shian Wu\r\nDepartment of Physiology\r\nNational Cheng Kung University Medical College\r\n", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 737, - "tag": "Heart disease" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 783, - "tag": "I_Ks" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 784, - "tag": "KCNQ1" - }, - { - "id": 785, - "tag": "Long-QT" - }, - { - "id": 748, - "tag": "Markov-type model" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1065, - "tag": "ModelDB:58581" - } - ], - "timestamp_created": "2024-01-11 13:52:47.156703+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/58581", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "595": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 595, - "name": "Coding of stimulus frequency by latency in thalamic networks (Golomb et al 2005)", - "repository_type": "github", - "summary": "The paper presents models of the rat vibrissa processing system including the\r\nposterior medial (POm) thalamus, ventroposterior medial (VPm) thalamus, and GABAB-\r\nmediated feedback inhibition from the reticular thalamic (Rt) nucleus.\r\nA clear match between the experimentally measured spike-rates and the\r\nnumerically calculated rates for the full model occurs when VPm thalamus receives stronger\r\nbrainstem input and weaker GABAB-mediated inhibition than POm thalamus.", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 791, - "tag": "Rate-coding model neurons" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 1066, - "tag": "ModelDB:58582" - } - ], - "timestamp_created": "2024-01-11 13:52:47.669002+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/58582", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "596": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 596, - "name": "Proximal inhibition of Renshaw cells (Bui et al 2005)", - "repository_type": "github", - "summary": "Inhibitory synaptic inputs to Renshaw cells are concentrated on the\r\nsoma and the juxtasomatic dendrites. In the present study, we \r\ninvestigated whether this proximal bias leads to more effective \r\ninhibition under different neuronal operating conditions. Using \r\ncompartmental models based on detailed anatomical measurements of \r\nintracellularly stained Renshaw cells, we compared the inhibition \r\nproduced by GABAA synapses when\r\ndistributed with a proximal bias to the inhibition produced when the \r\nsame synapses were distributed uniformly. See paper for more and details.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 792, - "tag": "SABER" - }, - { - "id": 1067, - "tag": "ModelDB:58957" - } - ], - "timestamp_created": "2024-01-11 13:52:48.235988+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/58957", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "597": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 597, - "name": "IA and IT interact to set first spike latency (Molineux et al 2005)", - "repository_type": "github", - "summary": "Using patch clamp and modeling, we illustrate that spike latency characteristics are the product of an interplay between I(A) and low-threshold calcium current (I(T)) that requires a steady-state difference in the inactivation parameters of the currents. Furthermore, we show that the unique first-spike latency characteristics of stellate cells have important implications for the integration of coincident IPSPs and EPSPs, such that inhibition can shift first-spike latency to differentially modulate the probability of firing.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 595, - "tag": "Coincidence Detection" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1068, - "tag": "ModelDB:59479" - } - ], - "timestamp_created": "2024-01-11 13:52:48.711053+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/59479", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "598": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 598, - "name": "Dendritic Na inactivation drives a decrease in ISI (Fernandez et al 2005)", - "repository_type": "github", - "summary": "We use a combination of dynamical analysis and electrophysiological recordings to demonstrate that spike broadening in dendrites is primarily caused by a cumulative inactivation of dendritic Na(+) current. We further show that a reduction in dendritic Na(+) current increases excitability by decreasing the interspike interval (ISI) and promoting burst firing.", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1069, - "tag": "ModelDB:59480" - } - ], - "timestamp_created": "2024-01-11 13:52:49.196010+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/59480", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "599": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 599, - "name": "Continuum model of tubulin-driven neurite elongation (Graham et al 2006)", - "repository_type": "github", - "summary": "This model investigates the elongation over time of a single developing neurite (axon or dendrite). Our neurite growth model describes the elongation of a single,unbranched neurite in terms of the rate of extension of the microtubule cytoskeleton. The cytoskeleton is not explicitly modelled, but its construction is assumed to depend on the\r\navailable free tubulin at the growing neurite tip.", - "tags": [ - { - "id": 793, - "tag": "Development" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 1070, - "tag": "ModelDB:59581" - } - ], - "timestamp_created": "2024-01-11 13:52:49.665739+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/59581", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "600": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 600, - "name": "Compartmental models of growing neurites (Graham and van Ooyen 2004)", - "repository_type": "github", - "summary": "Simulator for models of neurite outgrowth. The principle model is a biophysical model of neurite outgrowth described in Graham and van Ooyen (2004). In the model, branching depends on the concentration of a branch-determining substance in each terminal segment. The substance is produced in the cell body and is transported by active transport and diffusion to the terminals. The model reveals that transport-limited effects may give rise to the same modulation of branching as indicated by the stochastic BESTL model. Different limitations arise if transport is dominated by active transport or by diffusion.", - "tags": [ - { - "id": 793, - "tag": "Development" - }, - { - "id": 778, - "tag": "Java" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1071, - "tag": "ModelDB:59582" - } - ], - "timestamp_created": "2024-01-11 13:52:50.141815+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/59582", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "601": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 601, - "name": "Excitability of the soma in central nervous system neurons (Safronov et al 2000)", - "repository_type": "github", - "summary": "The ability of the soma of a spinal dorsal horn neuron, a spinal ventral horn neuron, and a hippocampal pyramidal neuron to generate action potentials was studied using experiments and computer simulations. By comparing recordings ... of a dorsal horn neuron with simulated responses, it was shown that computer models can be adequate for the study of somatic excitability. The modeled somata of both spinal neurons were unable to generate action potentials, showing only passive and local responses to current injections. ... In contrast to spinal neurons, the modeled soma of the hippocampal pyramidal neuron generated spikes with an overshoot of +9 mV. It is concluded that the somata of spinal neurons cannot generate action potentials and seem to resist their propagation from the axon to dendrites. ... See paper for more and details.\r\n", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1072, - "tag": "ModelDB:62266" - } - ], - "timestamp_created": "2024-01-11 13:52:50.609660+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/62266", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "602": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 602, - "name": "Inhibitory control by an integral feedback signal in prefrontal cortex (Miller and Wang 2006)", - "repository_type": "github", - "summary": "The prefrontal cortex (PFC) is known to be critical for inhibitory\r\ncontrol of behavior, but the underlying mechanisms are unclear.\r\nHere, we propose that inhibitory control can be instantiated by an\r\nintegral signal derived from working memory, another key function of the PFC. Specifically, we assume that an integrator converts\r\nexcitatory input into a graded mnemonic activity that provides an\r\ninhibitory signal (integral feedback control) to upstream afferent\r\nneurons. We demonstrate this scenario in a neuronal-network\r\nmodel for a temporal discrimination task... See paper for details\r\nand more.\r\n", - "tags": [ - { - "id": 771, - "tag": "Action Selection/Decision Making" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 776, - "tag": "C or Cplusplus program (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 719, - "tag": "Pattern Recognition" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 794, - "tag": "Working memory" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1073, - "tag": "ModelDB:62268" - } - ], - "timestamp_created": "2024-01-11 13:52:51.078840+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/62268", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "603": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 603, - "name": "A model for pituitary GH(3) lactotroph (Wu and Chang 2005)", - "repository_type": "github", - "summary": "The ATP-sensitive K(+) (K(ATP)) channels are composed of sulfonylurea receptor and inwardly rectifying K(+) channel (Kir6.2) subunit. These channels are regulated by intracellular ADP/ATP ratio and play a role in cellular metabolism. ... The objective of this study was to determine whether Diethyl pyrocarbonate (DEPC) modifies K(ATP)-channel activity in pituitary GH(3) cells. ... Simulation studies also demonstrated that the increased conductance of K(ATP)-channels used to mimic DEPC actions reduced the frequency of spontaneous action potentials and fluctuation of intracellular Ca(2+). The results indicate that chemical modification with DEPC enhances K(ATP)-channel activity and influences functional activities of pituitary GH(3) cells. See paper for more and details.", - "tags": [ - { - "id": 795, - "tag": "ATP-senstive potassium current" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1074, - "tag": "ModelDB:62272" - } - ], - "timestamp_created": "2024-01-11 15:22:07.585651+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/62272", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "604": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 604, - "name": "Spike frequency adaptation in spinal sensory neurones (Melnick et al 2004)", - "repository_type": "github", - "summary": "Using tight-seal recordings from rat spinal cord slices, intracellular\r\nlabelling and computer simulation, we analysed the mechanisms of spike\r\nfrequency adaptation in substantia gelatinosa (SG)\r\nneurones. Adapting-firing neurones (AFNs) generated short bursts of\r\nspikes during sustained depolarization and were mostly found in\r\nlateral SG. ... Ca2 + -dependent conductances do not contribute to\r\nadapting firing. Transient KA current was small and completely\r\ninactivated at resting potential suggesting that adapting firing was\r\nmainly generated by voltage-gated Na+ and delayed-rectifier K+ (KDR )\r\ncurrents. ... Computer simulation has further revealed that\r\ndown-regulation of Na+ conductance represents an effective mechanism\r\nfor the induction of firing adaptation. It is suggested that the\r\ncell-specific regulation of Na+ channel expression can be an important\r\nfactor underlying the diversity of firing patterns in SG neurones.\r\nSee paper for more and details.", - "tags": [ - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - }, - { - "id": 777, - "tag": "Spike Frequency Adaptation" - }, - { - "id": 1075, - "tag": "ModelDB:62284" - } - ], - "timestamp_created": "2024-01-11 15:22:08.295469+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/62284", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "605": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 605, - "name": "Tonic firing in substantia gelatinosa neurons (Melnick et al 2004)", - "repository_type": "github", - "summary": "Ionic conductances underlying excitability in tonically firing neurons\r\n(TFNs) from substantia gelatinosa (SG) were studied by the patch-clamp\r\nmethod in rat spinal cord slices. ... Suppression of Ca2+ and KCA currents ... did not\r\nabolish the basic pattern of tonic firing, indicating that it was\r\ngenerated by voltage-gated Na+ and K+ currents. ...\r\n on the basis of present data, we created a model of TFN\r\nand showed that Na+ and KDR currents are sufficient to generate a\r\nbasic pattern of tonic firing. It is concluded that the balanced\r\ncontribution of all ionic conductances described here is important for\r\ngeneration and modulation of tonic firing in SG neurons. See paper for more and details.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1076, - "tag": "ModelDB:62285" - } - ], - "timestamp_created": "2024-01-11 15:22:08.915964+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/62285", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "606": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 606, - "name": "Excitation-contraction coupling in an integrative heart cell model (Greenstein et al 2006)", - "repository_type": "github", - "summary": "\"... In this study, we generalize a recently developed analytical\r\napproach for deriving simplified mechanistic models of CICR\r\n(Ca(2+)-induced Ca(2+) release) to formulate an integrative model of\r\nthe canine cardiac myocyte which is computationally efficient. The\r\nresulting model faithfully reproduces experimentally measured\r\nproperties of EC (excitation-contraction) coupling and whole cell\r\nphenomena. The model is used to study the role of local redundancy in\r\nL-type Ca(2+) channel gating and the role of dyad configuration on EC\r\ncoupling. Simulations suggest that the characteristic steep rise in EC\r\ncoupling gain observed at hyperpolarized potentials is a result of\r\nincreased functional coupling between LCCs (L-type Ca(2+) channels)\r\nand RyRs (ryanodine-sensitive Ca(2+) release channels). We also\r\ndemonstrate mechanisms by which alterations in the early\r\nrepolarization phase of the action potential, resulting from reduction\r\nof the transient outward potassium current, alters properties of EC\r\ncoupling.\"", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1077, - "tag": "ModelDB:62286" - } - ], - "timestamp_created": "2024-01-11 15:22:09.413964+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/62286", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "607": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 607, - "name": "Kv4.3, Kv1.4 encoded K(+) channel in heart cells (Greenstein et al 2000) (XPP)", - "repository_type": "github", - "summary": "A model of canine I:(to1) (the Ca(2+)-independent transient outward current) is formulated as the combination of Kv4.3 and Kv1.4 \r\ncurrents and is incorporated into an existing canine ventricular myocyte model. Simulations demonstrate strong \r\ncoupling between L-type Ca(2+) current and I:(Kv4.3) and predict a bimodal relationship between I:(Kv4.3) \r\ndensity and APD whereby perturbations in I:(Kv4.3) density may produce either prolongation or shortening of APD, \r\ndepending on baseline I:(to1) current level.\r\nThe model files were submitted by:\r\nDr. Sheng-Nan Wu, Dr. Ruey J. Sung, Ya-Jean Wang and Jiun-Shian Wu\r\ne-mail: snwu@mail.ncku.edu.tw\r\n", - "tags": [ - { - "id": 737, - "tag": "Heart disease" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1078, - "tag": "ModelDB:62287" - } - ], - "timestamp_created": "2024-01-11 15:22:09.897044+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/62287", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "608": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 608, - "name": "Kv4.3, Kv1.4 encoded K channel in heart cells & tachy. (Winslow et al 1999, Greenstein et al 2000)", - "repository_type": "github", - "summary": "(1999) We present a model of the canine midmyocardial ventricular action potential and Ca2+ transient. The model is used to estimate the degree of functional upregulation and downregulation of Na/Ca exchanger protein and sarcoplasmic reticulum Ca ATPase in heart failure using data obtained from 2 different experimental protocols.\r\n(2000): A model of canine I:(to1) (the Ca(2+)-independent transient outward current) is formulated as the combination of Kv4.3 and Kv1.4 \r\ncurrents and is incorporated into an existing canine ventricular myocyte model. Simulations demonstrate strong \r\ncoupling between L-type Ca(2+) current and I:(Kv4.3) and predict a bimodal relationship between I:(Kv4.3) \r\ndensity and APD whereby perturbations in I:(Kv4.3) density may produce either prolongation or shortening of APD, \r\ndepending on baseline I:(to1) current level.\r\nSee each paper for more and details.\r\n", - "tags": [ - { - "id": 737, - "tag": "Heart disease" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 778, - "tag": "Java" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1079, - "tag": "ModelDB:62654" - } - ], - "timestamp_created": "2024-01-11 15:22:10.514560+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/62654", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "609": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 609, - "name": "Markovian model for cardiac sodium channel (Clancy, Rudy 2002)", - "repository_type": "github", - "summary": "Complex physiological interactions determine the functional consequences of gene abnormalities and make mechanistic interpretation of phenotypes extremely difficult. A recent example is a single mutation in the C terminus of the cardiac Na(+) channel, 1795insD. The mutation causes two distinct clinical syndromes, long QT (LQT) and Brugada, leading to life-threatening cardiac arrhythmias. Coexistence of these syndromes is seemingly paradoxical; LQT is associated with enhanced Na(+) channel function, and Brugada with reduced function. Using a computational approach, we demonstrate that the 1795insD mutation exerts variable effects depending on the myocardial substrate. We develop Markov models of the wild-type and 1795insD cardiac Na(+) channels. See reference for more and details. The model files were submitted by: Dr. Jiun-Shian Wu, Dr. Sheng-Nan Wu, Dr. Ruey J. Sung, Han-Dong Chang.", - "tags": [ - { - "id": 796, - "tag": "Brugada" - }, - { - "id": 737, - "tag": "Heart disease" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 785, - "tag": "Long-QT" - }, - { - "id": 748, - "tag": "Markov-type model" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 787, - "tag": "Pathophysiology" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1080, - "tag": "ModelDB:62661" - } - ], - "timestamp_created": "2024-01-11 15:22:10.988025+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/62661", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "610": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 610, - "name": "Sodium currents activate without a delay (Baranauskas and Martina 2006)", - "repository_type": "github", - "summary": "Hodgkin and Huxley established that sodium currents in the squid giant\r\naxons activate after a delay, which is explained by the model of a\r\nchannel with three identical independent gates that all have to open\r\nbefore the channel can pass current (the HH model). It is assumed that\r\nthis model can adequately describe the sodium current activation time\r\ncourse in all mammalian central neurons, although there is no\r\nexperimental evidence to support such a conjecture. We performed high\r\ntemporal resolution studies of sodium currents gating in three types\r\nof central neurons. ... These results can be explained by a model with\r\ntwo closed states and one open state. ... This\r\nmodel captures all major properties of the sodium current\r\nactivation. In addition, the proposed model reproduces the observed\r\naction potential shape more accurately than the traditional HH model.\r\nSee paper for more and details.\r\n", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1081, - "tag": "ModelDB:62673" - } - ], - "timestamp_created": "2024-01-11 15:22:11.792198+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/62673", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "611": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 611, - "name": "Markovian model for single-channel recordings of Ik_1 in ventricular cells (Matsuoka et al 2003)", - "repository_type": "github", - "summary": "The interaction between many currents in a cardiac ventricular model are examined in this paper. One of the main contributions come from a current called IK_1. An XPP version of this model was supplied by\r\nHsieng-Jung Lai, Jiun-Shian Wu, Sheng-Nan Wu, Ruey J. Sung, Han-Dong\r\nChang. Please see paper and model for more and details.", - "tags": [ - { - "id": 797, - "tag": "Cardiac pacemaking" - }, - { - "id": 737, - "tag": "Heart disease" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 748, - "tag": "Markov-type model" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1082, - "tag": "ModelDB:62676" - } - ], - "timestamp_created": "2024-01-11 15:22:12.363164+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/62676", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "612": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 612, - "name": "Dichotomy of action-potential backpropagation in CA1 pyramidal neuron dendrites (Golding et al 2001)", - "repository_type": "github", - "summary": "From reference below and \r\nCorrigendum: J Neurophysiol 87:1a, 2002 (better versions of figures 2, 3, 5 and 7 because of poor print quality in the original article; as of 2/2006, these figures are perfectly fine in the PDF of the original article that is currently available from the publisher's WWW site).\r\nExamines the anatomical and biophysical factors that account for the fact that retrograde invasion of spikes into the apical dendritic tree past 300 um succeeds in some CA1 pyramidal neurons but fails in others.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 743, - "tag": "NEURON (web link to model)" - }, - { - "id": 1083, - "tag": "ModelDB:64167" - } - ], - "timestamp_created": "2024-01-11 15:22:12.877191+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/64167", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "613": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 613, - "name": "Voltage attenuation in CA1 pyramidal neuron dendrites (Golding et al 2005)", - "repository_type": "github", - "summary": "Voltage attenuation in the apical dendritic field of CA1 pyramidal neurons is particularly strong for epsps spreading toward the soma. High cytoplasmic resistivity and high membrane (leak) conductance appear to be the major determinants of voltage attenuation over most of the apical field, but H current may be responsible for as much as half of the attenuation of distal apical epsps.", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 786, - "tag": "Electrotonus" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 743, - "tag": "NEURON (web link to model)" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 1084, - "tag": "ModelDB:64170" - } - ], - "timestamp_created": "2024-01-11 15:22:13.364450+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/64170", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "614": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 614, - "name": "Cardiac action potential based on Luo-Rudy phase 1 model (Luo and Rudy 1991), (Wu 2004)", - "repository_type": "github", - "summary": "A mathematical model of the membrane action potential of the mammalian \r\nventricular cell is introduced. The model is based, whenever possible, \r\non recent single-cell and single-channel data and incorporates the \r\npossibility of changing extracellular potassium concentration [K]o. The \r\nfast sodium current, INa, is characterized by fast upstroke velocity (Vmax \r\n= 400 V/sec) and slow recovery from inactivation. The time-independent \r\npotassium current, IK1, includes a negative-slope phase and displays \r\nsignificant crossover phenomenon as [K]o is varied. The time-dependent \r\npotassium current, IK, shows only a minimal degree of crossover. A novel \r\npotassium current that activates at plateau potentials is included in \r\nthe model. The simulated action potential duplicates the experimentally \r\nobserved effects of changes in [K]o on action potential duration and rest \r\npotential. See papers for more and details.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 737, - "tag": "Heart disease" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 787, - "tag": "Pathophysiology" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1085, - "tag": "ModelDB:64171" - } - ], - "timestamp_created": "2024-01-11 15:22:13.841154+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/64171", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "615": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 615, - "name": "Stochastic Ih and Na-channels in pyramidal neuron dendrites (Kole et al 2006)", - "repository_type": "github", - "summary": "The hyperpolarization-activated cation current (Ih) plays an important role in regulating neuronal excitability, yet its native single-channel properties in the brain are essentially unknown. Here we use variance-mean analysis to study the properties of single Ih channels in the apical dendrites of cortical layer 5 pyramidal neurons in vitro. ... In contrast to the uniformly distributed single-channel conductance, Ih channel number increases exponentially with distance, reaching densities as high as approximately 550 channels/microm2 at distal dendritic sites. These high channel densities generate significant membrane voltage noise. By incorporating a stochastic model of Ih single-channel gating into a morphologically realistic model of a layer 5 neuron, we show that this channel noise is higher in distal dendritic compartments and increased threefold with a 10-fold increased single-channel conductance (6.8 pS) but constant Ih current density. ... These data suggest that, in the face of high current densities, the small single-channel conductance of Ih is critical for maintaining the fidelity of action potential output. See paper for more and details.", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1086, - "tag": "ModelDB:64195" - } - ], - "timestamp_created": "2024-01-11 15:22:14.310358+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/64195", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "616": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 616, - "name": "Vomeronasal sensory neuron (Shimazaki et al 2006)", - "repository_type": "github", - "summary": "NEURON model files from the papers:\r\nShimazaki et al, Chem. Senses, epub ahead of print (2006)\r\nElectrophysiological properties and modeling of murine vomeronasal \r\nsensory neurons in acute slice preparations.\r\n\r\nThe model reproduces quantitatively the experimentally observed \r\nfiring rates of these neurons under a wide range of input currents.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1087, - "tag": "ModelDB:64212" - } - ], - "timestamp_created": "2024-01-11 15:22:14.773227+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/64212", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "617": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 617, - "name": "Reciprocal regulation of rod and cone synapse by NO (Kourennyi et al 2004)", - "repository_type": "github", - "summary": "We constructed models of rod and cone photoreceptors\r\nusing NEURON software to predict how changes in Ca channels\r\nwould affect the light response in these cells and in\r\npostsynaptic horizontal cells.", - "tags": [ - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 765, - "tag": "I Chloride" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 726, - "tag": "Vision" - }, - { - "id": 1088, - "tag": "ModelDB:64216" - } - ], - "timestamp_created": "2024-01-11 15:22:15.341640+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/64216", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "618": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 618, - "name": "Simulated light response in rod photoreceptors (Liu and Kourennyi 2004)", - "repository_type": "github", - "summary": "We developed a complete computer model of the rod, which accurately reproduced the main features of the light response and allowed us to demonstrate that it was suppression of Kx channels that was essential for slowing SLR and increasing excitability of rods. The results reported in this work further establish the importance of Kx channels in rod photoreceptor function.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 765, - "tag": "I Chloride" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 1089, - "tag": "ModelDB:64228" - } - ], - "timestamp_created": "2024-01-11 15:22:15.852965+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/64228", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "619": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 619, - "name": "Parallel network simulations with NEURON (Migliore et al 2006)", - "repository_type": "github", - "summary": "The NEURON simulation environment has been extended to support parallel network simulations.\r\nThe performance of three published network models with very different spike patterns exhibits superlinear speedup on Beowulf clusters.\r\n", - "tags": [ - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1090, - "tag": "ModelDB:64229" - } - ], - "timestamp_created": "2024-01-11 15:22:16.346398+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/64229", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "620": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 620, - "name": "Biologically-plausible models for spatial navigation (Cannon et al 2003)", - "repository_type": "github", - "summary": "Hypotheses about how parahippocampal and hippocampal structures may be involved in spatial navigation tasks are implemented in a model of a virtual rat navigating through a virtual environment in search of a food reward. The model incorporates theta oscillations to separate encoding from retrieval and yields testable predictions about the phase relations of spiking activity to theta oscillations in different parts of the hippocampal formation at various stages of the behavioral task. See paper for more and details.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 798, - "tag": "Catacomb (web link to model)" - }, - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 799, - "tag": "Place cell/field" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 800, - "tag": "Spatial Navigation" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 1091, - "tag": "ModelDB:64242" - } - ], - "timestamp_created": "2024-01-11 15:22:16.825472+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/64242", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "621": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 621, - "name": "A dynamical model of the basal ganglia (Leblois et al 2006)", - "repository_type": "github", - "summary": "We propose a new model for the function and dysfunction of the basal ganglia (BG). \r\nThe basal ganglia are a set of cerebral structures involved in motor control which \r\ndysfunction causes high-incidence pathologies such as Parkinson's disease (PD). \r\nTheir precise motor functions remain unknown. \r\nThe classical model of the BG that allowed for the discovery of new treatments \r\nfor PD seems today outdated in several respects. \r\nBased on experimental observations, our model proposes a simple dynamical framework \r\nfor the understanding of how BG may select motor programs to be executed. Moreover, \r\nwe explain how this ability is lost and how tremor-related oscillations in neuronal \r\nactivity may emerge in PD. \r\n", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 787, - "tag": "Pathophysiology" - }, - { - "id": 791, - "tag": "Rate-coding model neurons" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 1092, - "tag": "ModelDB:64255" - } - ], - "timestamp_created": "2024-01-11 15:22:17.321164+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/64255", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "622": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 622, - "name": "Steady-state Vm distribution of neurons subject to synaptic noise (Rudolph, Destexhe 2005)", - "repository_type": "github", - "summary": "This package simulates synaptic background activity similar to in vivo measurements using a model of fluctuating synaptic conductances, and compares the simulations with analytic estimates. The steady-state membrane potential (Vm) distribution is calculated numerically and compared with the \"extended\" analytic expression provided in the reference (see this paper for details).", - "tags": [ - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 744, - "tag": "Synaptic noise" - }, - { - "id": 1093, - "tag": "ModelDB:64259" - } - ], - "timestamp_created": "2024-01-11 15:22:17.781681+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/64259", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "623": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 623, - "name": "Learning spatial transformations through STDP (Davison, Fr\u00e9gnac 2006)", - "repository_type": "github", - "summary": "A common problem in tasks involving the integration of spatial information from multiple senses, or in sensorimotor coordination, is that different modalities represent space in different frames of reference. Coordinate transformations between different reference frames are therefore required. One way to achieve this relies on the encoding of spatial information using population codes. The set of network responses to stimuli in different locations (tuning curves) constitute a basis set of functions which can be combined\r\nlinearly through weighted synaptic connections in order to approximate non-linear transformations of the input variables. The question then arises how the appropriate synaptic connectivity is obtained. \r\n\r\nThis model shows that a network of spiking neurons can learn the coordinate transformation from one frame of reference to another, with connectivity that develops continuously in an unsupervised manner, based only on the correlations available in the environment, and with a biologically-realistic plasticity mechanism (spike timing-dependent plasticity).", - "tags": [ - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 802, - "tag": "STDP" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 803, - "tag": "Unsupervised Learning" - }, - { - "id": 1094, - "tag": "ModelDB:64261" - } - ], - "timestamp_created": "2024-01-11 15:22:18.253545+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/64261", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "624": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 624, - "name": "Simple model of barrel-specific segregation in cortex (Lu et al 2006)", - "repository_type": "github", - "summary": "Mice with a loss-of-function mutation of calcium/calmodulin-activated adenylyl cyclase I (AC1) - barrelless mice - have strikingly abherrent cortical development: the thalamic afferents into the barrel cortex do not segregate into whisker-specific barrels. Our paper investigates the link between this mutation and the \"barrelless\" phenotype, and demonstrates that the loss-of-function mutation leads to deficits in presynaptic mechanisms at the thalamocortical synapse.\r\n\r\nHow might presynaptic deficits disrupt whisker-specific segregation in the barrel cortex? We used a model to demonstrate one possibility: decrease in the release probability at the thalamocortical synapse (which is observed in the barrelless mutant) can influence the balance between LTP and LTD (in favor of LTD), which can disrupt whisker segregaton. Though how this occurs is easily explained with a conceptual model (described succinctly in the associated paper), we also produced a computational simulation of this phenomenon.", - "tags": [ - { - "id": 776, - "tag": "C or Cplusplus program (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1095, - "tag": "ModelDB:64266" - } - ], - "timestamp_created": "2024-01-11 15:22:18.859646+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/64266", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "625": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 625, - "name": "Dopaminergic cell bursting model (Kuznetsov et al 2006)", - "repository_type": "github", - "summary": "Dopaminergic neurons of the midbrain fire spontaneously at rates\r\n<10/s and ordinarily will not exceed this range even when driven with\r\nsomatic current injection. During spontaneous\r\nbursting of dopaminergic neurons in vivo, bursts related to reward\r\nexpectation in behaving animals, and bursts generated by dendritic\r\napplication of N-methyl-D-aspartate (NMDA) agonists, transient firing\r\nattains rates well above this range. We suggest a way such highfrequency\r\nfiring may occur in response to dendritic NMDA receptor\r\nactivation. We have extended the coupled oscillator model of the\r\ndopaminergic neuron, which represents the soma and dendrites as\r\nelectrically coupled compartments with different natural spiking frequencies,\r\nby addition of dendritic AMPA (voltage-independent) or\r\nNMDA (voltage-dependent) synaptic conductance. Both soma and\r\ndendrites contain a simplified version of the calcium-potassium mechanism\r\nknown to be the mechanism for slow spontaneous oscillation\r\nand background firing in dopaminergic cells. We show that because of its voltage dependence,\r\nNMDA receptor activation acts to amplify the effect on the\r\nsoma of the high-frequency oscillation of the dendrites, which is\r\nnormally too weak to exert a large influence on the overall oscillation\r\nfrequency of the neuron.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1096, - "tag": "ModelDB:64285" - } - ], - "timestamp_created": "2024-01-11 15:22:19.427550+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/64285", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "626": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 626, - "name": "Dynamical model of olfactory bulb mitral cell (Rubin, Cleland 2006)", - "repository_type": "github", - "summary": "This four-compartment mitral cell exhibits endogenous subthreshold oscillations, phase resetting, and evoked spike phasing properties as described in electrophysiological studies of mitral cells. It is derived from the prior work of Davison et al (2000) and Bhalla and Bower (1993). See readme.txt for details.", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 754, - "tag": "Delay" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 1097, - "tag": "ModelDB:64296" - } - ], - "timestamp_created": "2024-01-11 15:22:19.965472+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/64296", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "627": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 627, - "name": "Computational Model of a Central Pattern Generator (Cataldo et al 2006)", - "repository_type": "github", - "summary": "The buccal ganglia of Aplysia contain a central pattern generator (CPG) that mediates rhythmic movements of the foregut during feeding. This CPG is a multifunctional circuit and generates at least two types of buccal motor patterns (BMPs), one that mediates ingestion (iBMP) and another that mediates rejection (rBMP). The present study used a computational approach to examine the ways in which an ensemble of identified cells and synaptic connections function as a CPG. Hodgkin-Huxley-type models were developed that mimicked the biophysical properties of these cells and synaptic connections. The results suggest that the currently identified ensemble of cells is inadequate to produce rhythmic neural activity and that several key elements of the CPG remain to be identified.", - "tags": [ - { - "id": 765, - "tag": "I Chloride" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 757, - "tag": "SNNAP" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 1098, - "tag": "ModelDB:65412" - } - ], - "timestamp_created": "2024-01-11 15:22:20.460767+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/65412", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "628": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 628, - "name": "CA1 pyramidal cell: I_NaP and I_M contributions to somatic bursting (Golomb et al 2006)", - "repository_type": "github", - "summary": "To study the mechanisms of bursting, we have constructed a\r\nconductance-based, one-compartment model of CA1 pyramidal neurons. In this neuron model,\r\nreduced [Ca2+]o is simulated by negatively shifting the activation curve of the persistent Na+ current\r\n(INaP), as indicated by recent experimental results. The neuron model accounts, with different\r\nparameter sets, for the diversity of firing patterns observed experimentally in both zero and normal\r\n[Ca2+]o. Increasing INaP in the neuron model induces bursting and increases the number of spikes\r\nwithin a burst, but is neither necessary nor sufficient for bursting. We show, using fast-slow analysis\r\nand bifurcation theory, that the M-type K+ current (IM) allows bursting by shifting neuronal behavior\r\nbetween a silent and a tonically-active state, provided the kinetics of the spike generating currents are\r\nsufficiently, though not extremely, fast. We suggest that bursting in CA1 pyramidal cells can be\r\nexplained by a single compartment *square bursting* mechanism with one slow variable, the\r\nactivation of IM. See paper for more and details.", - "tags": [ - { - "id": 804, - "tag": "Bifurcation" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1099, - "tag": "ModelDB:66268" - } - ], - "timestamp_created": "2024-01-11 15:22:20.979740+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/66268", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "629": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 629, - "name": "CA1 pyramidal neuron synaptic integration (Li and Ascoli 2006, 2008)", - "repository_type": "github", - "summary": "The model shows how different input patterns (irregular & asynchronous,\r\nirregular & synchronous, regular & asynchronous, regular & synchronous)\r\naffect the neuron's output rate when 1000 synapses are distributed in\r\nthe proximal apical dendritic tree of a hippocampus CA1 pyramidal neuron.", - "tags": [ - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 1100, - "tag": "ModelDB:71312" - } - ], - "timestamp_created": "2024-01-11 15:22:21.468777+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/71312", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "630": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 630, - "name": "Activity dependent changes in motoneurones (Dai Y et al 2002, Gardiner et al 2002)", - "repository_type": "github", - "summary": "These two papers review various experimental papers and examine the effects of activity on motoneurons in a similar 5 compartment model with 10 active conductances. Included are slow (S) and fast (F) type and fast fatigue resistant (FR) and fast fatigable (FF) models corresponding to the types of motoneurons. See papers for more and details.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 732, - "tag": "Tutorial/Teaching" - }, - { - "id": 1101, - "tag": "ModelDB:71317" - } - ], - "timestamp_created": "2024-01-11 15:22:22.098138+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/71317", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "631": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 631, - "name": "Rat subthalamic projection neuron (Gillies and Willshaw 2006)", - "repository_type": "github", - "summary": "A computational model of the rat subthalamic nucleus projection neuron is constructed using electrophysiological and morphological data and a restricted set of channel specifications. The model cell exhibits a wide range of electrophysiological behaviors characteristic of rat subthalamic neurons. It reveals that a key set of three channels play a primary role in distinguishing behaviors: a high-voltage-activated calcium channel (Cav 1.2.-1.3), a low-voltage-activated calcium channel (Cav 3.-), and a small current calcium-activated potassium channel (KCa 2.1-2.3). See paper for more and details.", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 805, - "tag": "I Mixed" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1102, - "tag": "ModelDB:74298" - } - ], - "timestamp_created": "2024-01-11 15:22:22.619501+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/74298", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "632": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 632, - "name": "Excitatory and inhibitory interactions in populations of model neurons (Wilson and Cowan 1972)", - "repository_type": "github", - "summary": "Coupled nonlinear differential equations are derived for the dynamics\r\nof spatially localized populations containing both excitatory and inhibitory model\r\nneurons. Phase plane methods and numerical solutions are then used to investigate\r\npopulation responses to various types of stimuli. The results obtained show simple\r\nand multiple hysteresis phenomena and limit cycle activity. The latter is particularly\r\ninteresting since the frequency of the limit cycle oscillation is found to be a monotonic\r\nfunction of stimulus intensity. Finally, it is proved that the existence of limit cycle\r\ndynamics in response to one class of stimuli implies the existence of multiple stable\r\nstates and hysteresis in response to a different class of stimuli. The relation between\r\nthese findings and a number of experiments is discussed.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1103, - "tag": "ModelDB:76879" - } - ], - "timestamp_created": "2024-01-11 15:22:23.102643+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/76879", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "633": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 633, - "name": "Scaling self-organizing maps to model large cortical networks (Bednar et al 2004)", - "repository_type": "github", - "summary": "Self-organizing computational models\r\nwith specific intracortical connections can\r\nexplain many functional features of visual\r\ncortex, such as topographic orientation and\r\nocular dominance maps. ... This article\r\nintroduces two techniques that make large simulations\r\npractical. \r\n\r\nFirst, we show how parameter\r\nscaling equations can be derived for\r\nlaterally connected self-organizing models.\r\nThese equations result in quantitatively equivalent\r\nmaps over a wide range of simulation\r\nsizes, making it possible to debug small simulations\r\nand then scale them up only when\r\nneeded. ...\r\nSecond, we use parameter\r\nscaling to implement a new growing map\r\nmethod called GLISSOM, which dramatically\r\nreduces the memory and computational\r\nrequirements of large self-organizing networks.\r\n\r\nSee paper for more and details.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 806, - "tag": "Topographica (web link to model)" - }, - { - "id": 803, - "tag": "Unsupervised Learning" - }, - { - "id": 1104, - "tag": "ModelDB:76883" - } - ], - "timestamp_created": "2024-01-11 15:22:23.722914+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/76883", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "634": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 634, - "name": "An oscillatory neural model of multiple object tracking (Kazanovich and Borisyuk 2006)", - "repository_type": "github", - "summary": "An oscillatory neural network model of multiple object tracking is described. The model works with a set of identical visual objects moving around the screen. At the initial stage, the model selects into the focus of attention a subset of objects initially marked as targets. Other objects are used as distractors. The model aims to preserve the initial separation between targets and distractors while objects are moving. This is achieved by a proper interplay of synchronizing and desynchronizing interactions in a multilayer network, where each layer is responsible for tracking a single target. The results of the model simulation are presented and compared with experimental data. In agreement with experimental evidence, simulations with a larger number of targets have shown higher error rates. Also, the functioning of the model in the case of temporarily overlapping objects is presented.", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 1105, - "tag": "ModelDB:79145" - } - ], - "timestamp_created": "2024-01-11 15:22:24.243334+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/79145", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "635": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 635, - "name": "Simulation study of Andersen-Tawil syndrome (Sung et al 2006)", - "repository_type": "github", - "summary": "Patients with Andersen-Tawil syndrome (ATS) mostly have mutations on the KCNJ2 gene producing loss of function or \r\ndominant-negative suppression of the inward rectifier K(+) channel Kir2.1. However, clinical manifestations of ATS \r\nincluding dysmorphic features, periodic paralysis (hypo-, hyper-, or normokalemic), long QT, and ventricular arrhythmias \r\n(VA) are considerably variable. Using a modified dynamic Luo-Rudy simulation model of cardiac ventricular myocyte, we \r\nelucidate the mechanisms of VA in ATS. We adopted a kinetic model of KCNJ2 in which channel block by Mg(+2) and \r\nspermine was incorporated. In this study, we attempt to examine the effects of KCNJ2 mutations on the ventricular action \r\npotential (AP), single-channel Markovian models were reformulated and incorporated into the dynamic Luo-Rudy model \r\nfor rapidly and slowly delayed rectifying K(+) currents and KCNJ2 channel. During pacing at 1.0 Hz with [K(+)]o at 5.4 \r\nmM, a stepwise 10% reduction of Kir2.1 channel conductance progressively prolonged the terminal repolarization phase \r\nof AP along with gradual depolarization of the resting membrane potential (RMP). At 90% reduction, early after-\r\ndepolarizations (EADs) became inducible and RMP was depolarized to -55.0 mV (control: -90.1 mV) followed by \r\nemergence of spontaneous action potentials (SAP). Both EADs and SAP were facilitated by a decrease in [K(+)]o and \r\nsuppressed by increase in [K(+)]o. beta-adrenergic stimulation enhanced delayed after-depolarizations (DADs) and could \r\nalso facilitate EADs as well as SAP in the setting of low [K(+)]o and reduced Kir2.1 channel conductance. In conclusion, \r\nthe spectrum of VA in ATS includes (1) triggered activity mediated by EADs and/or DADs, and (2) abnormal automaticity \r\nmanifested as SAP. These VA can be aggravated by a decrease in [K(+)]o and beta-adrenergic stimulation, and may \r\npotentially induce torsades de pointes and cause sudden death. In patients with ATS, the hypokalemic form of periodic \r\nparalysis should have the highest propensity to VA especially during physical activities.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 737, - "tag": "Heart disease" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 805, - "tag": "I Mixed" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 785, - "tag": "Long-QT" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 740, - "tag": "Na/K pump" - }, - { - "id": 741, - "tag": "Sodium pump" - }, - { - "id": 1106, - "tag": "ModelDB:79237" - } - ], - "timestamp_created": "2024-01-11 15:22:24.760362+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/79237", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "636": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 636, - "name": "Action potential of striated muscle fiber (Adrian et al 1970)", - "repository_type": "github", - "summary": "1. Membrane currents during step depolarizations were determined by \r\na method in which three electrodes were inserted near the end of a \r\nfibre in the frog's sartorius muscle. The theoretical basis and \r\nlimitations of the method are discussed.\r\n2. Measurements of the membrane capacity (CM) and resting resistance \r\n(RM) derived from the current during a step change in membrane \r\npotential are consistent with values found by other methods.\r\n3. In fibres made mechanically inactive with hypertonic solutions \r\n(Ringer solution plus 350 mM sucrose) step depolarizations produced \r\nionic currents which resembled those of nerve in showing (a) an early \r\ntransient inward current, abolished by tetrodotoxin, which reversed \r\nwhen the depolarization was carried beyond an internal potential of \r\nabout +20 mV, (b) a delayed outward current, with a linear instantaneous \r\ncurrent\u00a1Xvoltage relation, and a mean equilibrium potential with a normal \r\npotassium concentration (2\u00a1P5 mM) of -85 mV.\r\n4. The reversal potential for the early current appears to be consistent\r\nwith the sodium equilibrium potential expected in hypertonic solutions.\r\n5. The variation of the equilibrium potential for the delayed current \r\n(V\u00a1\u00acK) with external potassium concentration suggests that the channel \r\nfor delayed current has a ratio of potassium to sodium permeability of \r\n30:1; this is less than the resting membrane where the ratio appears \r\nto be 100:1. V\u00a1\u00acK corresponds well with the membrane potential at the \r\nbeginning of the negative after-potential observed under similar conditions.\r\n6. The variation of V\u00a1\u00acK with the amount of current which has passed \r\nthrough the delayed channel suggests that potassium ions accumulate in a \r\nspace of between 1/3 and 1/6 of the fibre volume. If potassium accumulates in \r\nthe transverse tubular system (T system) much greater variation in V\u00a1\u00acK \r\nwould be expected.\r\n7. The delayed current is not maintained but is inactivated like the early \r\ncurrent. The inactivation is approximately exponential with a time constant \r\nof 0\u00a1P5 to 1 sec at 20\u00a2X C. The steady-state inactivation of the potassium \r\ncurrent is similar to that for the sodium current, but its voltage \r\ndependence is less steep and the potential for half inactivation is 20 mV \r\nrate more positive.\r\n8. Reconstructions of ionic currents were made in terms of the parameters\r\n(m, n, h) of the Hodgkin\u00a1XHuxley model for the squid axon, using constants \r\nwhich showed a similar dependence on voltage.\r\n9. Propagated action potentials and conduction velocities were computed for \r\nvarious conditions on the assumption that the T system behaves as if it were \r\na series resistance and capacity in parallel with surface capacity and the \r\nchannels for sodium, potassium and leak current. There was reasonable \r\nagreement with observed values, the main difference being that the \r\ncalculated velocities and rates of rise were somewhat less than those \r\nobserved experimentally.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1107, - "tag": "ModelDB:79238" - } - ], - "timestamp_created": "2024-01-11 15:22:25.357424+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/79238", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "637": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 637, - "name": "Cardiac Atrial Cell (Courtemanche et al 1998) (C++)", - "repository_type": "github", - "summary": "The mechanisms underlying many important properties of the human atrial action potential (AP) \r\nare poorly understood. Using specific formulations of the K+, Na+, and Ca2+ currents based on \r\ndata recorded from human atrial myocytes, along with representations of pump, exchange, and \r\nbackground currents, we developed a mathematical model of the AP. The model AP resembles APs \r\nrecorded from human atrial samples and responds to rate changes, L-type Ca2+ current blockade, \r\nNa+/Ca2+ exchanger inhibition, and variations in transient outward current amplitude in a \r\nfashion similar to experimental recordings. Rate-dependent adaptation of AP duration, an \r\nimportant determinant of susceptibility to atrial fibrillation, was attributable to \r\nincomplete L-type Ca2+ current recovery from inactivation and incomplete delayed rectifier \r\ncurrent deactivation at rapid rates. Experimental observations of variable AP morphology \r\ncould be accounted for by changes in transient outward current density, as suggested \r\nexperimentally. We conclude that this mathematical model of the human atrial AP reproduces \r\na variety of observed AP behaviors and provides insights into the mechanisms of clinically \r\nimportant AP properties.\r\n", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 737, - "tag": "Heart disease" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 740, - "tag": "Na/K pump" - }, - { - "id": 787, - "tag": "Pathophysiology" - }, - { - "id": 741, - "tag": "Sodium pump" - }, - { - "id": 1108, - "tag": "ModelDB:79461" - } - ], - "timestamp_created": "2024-01-11 15:22:25.892073+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/79461", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "638": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 638, - "name": "FS Striatal interneuron: K currents solve signal-to-noise problems (Kotaleski et al 2006)", - "repository_type": "github", - "summary": "... We show that a transient\r\npotassium (KA) current allows the Fast Spiking (FS) interneuron to strike a balance\r\nbetween sensitivity to correlated input and robustness to noise,\r\nthereby increasing its signal-to-noise ratio (SNR). First, a compartmental\r\nFS neuron model was created to match experimental data from\r\nstriatal FS interneurons in cortex\u2013striatum\u2013substantia nigra organotypic\r\ncultures. Densities of sodium, delayed rectifier, and KA channels\r\nwere optimized to replicate responses to somatic current injection.\r\nSpontaneous AMPA and GABA synaptic currents were\r\nadjusted to the experimentally measured amplitude, rise time, and\r\ninterevent interval histograms. Second, two additional adjustments\r\nwere required to emulate the remaining experimental observations.\r\nGABA channels were localized closer to the soma than AMPA\r\nchannels to match the synaptic population reversal potential. Correlation\r\namong inputs was required to produce the observed firing rate\r\nduring up-states. In this final model, KA channels were essential for\r\nsuppressing down-state spikes while allowing reliable spike generation\r\nduring up-states. ... Our results suggest that KA\r\nchannels allow FS interneurons to operate without a decrease in SNR\r\nduring conditions of increased dopamine, as occurs in response to\r\nreward or anticipated reward. See paper for more and details.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 1109, - "tag": "ModelDB:79465" - } - ], - "timestamp_created": "2024-01-11 15:22:26.460855+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/79465", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "639": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 639, - "name": "Differences between type A and B photoreceptors (Blackwell 2006)", - "repository_type": "github", - "summary": "In Hermissenda crassicornis, the memory of light associated with turbulence\r\nis stored as changes in intrinsic and synaptic currents in both\r\ntype A and type B photoreceptors. These photoreceptor types exhibit\r\nqualitatively different responses to light and current injection, and\r\nthese differences shape the spatiotemporal firing patterns that control\r\nbehavior. Thus the objective of the study was to identify the mechanisms\r\nunderlying these differences. The approach was to develop a\r\ntype B model that reproduced characteristics of type B photoreceptors\r\nrecorded in vitro, and then to create a type A model by modifying a\r\nselect number of ionic currents. Comparison of type A models with\r\ncharacteristics of type A photoreceptors recorded in vitro revealed that\r\ntype A and type B photoreceptors have five main differences, three\r\nthat have been characterized experimentally and two that constitute\r\nhypotheses to be tested with experiments in the future. See paper for more and details.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 774, - "tag": "Chemesis" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1110, - "tag": "ModelDB:79471" - } - ], - "timestamp_created": "2024-01-11 15:22:26.947840+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/79471", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "640": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 640, - "name": "Dynamic dopamine modulation in the basal ganglia: Learning in Parkinson (Frank et al 2004,2005)", - "repository_type": "github", - "summary": "See README file for all info on how to run models under different tasks and simulated Parkinson's and medication conditions.", - "tags": [ - { - "id": 771, - "tag": "Action Selection/Decision Making" - }, - { - "id": 807, - "tag": "Emergent/PDPplusplus" - }, - { - "id": 808, - "tag": "Hebbian plasticity" - }, - { - "id": 780, - "tag": "I Cl,Ca" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 787, - "tag": "Pathophysiology" - }, - { - "id": 791, - "tag": "Rate-coding model neurons" - }, - { - "id": 809, - "tag": "Reinforcement Learning" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 1111, - "tag": "ModelDB:79488" - } - ], - "timestamp_created": "2024-01-11 15:23:48.822874+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/79488", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "641": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 641, - "name": "Cerebellar purkinje cell: interacting Kv3 and Na currents influence firing (Akemann, Knopfel 2006)", - "repository_type": "github", - "summary": "Purkinje neurons spontaneously generate action potentials in the absence of synaptic drive and thereby exert a tonic, yet plastic, input to their target cells in the deep cerebellar nuclei. Purkinje neurons express two ionic currents with biophysical properties that are specialized for high-frequency firing: resurgent sodium currents and potassium currents mediated by Kv3.3. Numerical simulations indicated that Kv3.3 increases the spontaneous firing rate via cooperation with resurgent sodium currents. We conclude that the rate of spontaneous action potential firing of Purkinje neurons is controlled by the interaction of Kv3.3 potassium currents and resurgent sodium currents. See paper for more and details.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 1112, - "tag": "ModelDB:80769" - } - ], - "timestamp_created": "2024-01-11 15:23:49.444066+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/80769", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "642": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 642, - "name": "Ih levels roles in bursting and regular-spiking subiculum pyramidal neurons (van Welie et al 2006)", - "repository_type": "github", - "summary": "Pyramidal neurons in the subiculum typically display either bursting\r\nor regular-spiking behavior. ... Here we report that bursting neurons\r\nposses a hyperpolarization-activated cation current (Ih) that is\r\ntwo-fold larger (conductance: 5.3 \u00c2\u00b1 0.5 nS) than in regularspiking\r\nneurons (2.2 \u00c2\u00b1 0.6 nS), while Ih exhibits similar voltage-dependent\r\nand kinetic properties in both classes of neurons. Bursting and\r\nregular-spiking neurons display similar morphology. The difference in\r\nIh between the two classes is not responsible for the distinct firing\r\npatterns, since neither pharmacological blockade of Ih nor enhancement\r\nof Ih using a dynamic clamp affects the qualitative firing\r\npatterns. Instead, the difference in Ih between bursting and\r\nregular-spiking neurons determines the temporal integration of evoked\r\nsynaptic input from the CA1 area. In response to 50 Hz stimulation,\r\nbursting neurons, with a large Ih, show ~50% less temporal summation\r\nthan regular-spiking neurons. ... A computer simulation model of a\r\nsubicular neuron with the properties of either a bursting or a\r\nregular-spiking neuron confirmed the pivotal role of Ih in temporal\r\nintegration of synaptic input. These data suggest that in the\r\nsubicular network, bursting neurons are better suited to discriminate\r\nthe content of high frequency input, such as that occurring during\r\ngamma oscillations, compared to regular-spiking neurons. See paper for more and details.\r\n", - "tags": [ - { - "id": 595, - "tag": "Coincidence Detection" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 1113, - "tag": "ModelDB:82364" - } - ], - "timestamp_created": "2024-01-11 15:23:49.935286+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/82364", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "643": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 643, - "name": "Information-processing in lamina-specific cortical microcircuits (Haeusler and Maass 2006)", - "repository_type": "github", - "summary": "A major challenge for computational neuroscience is to understand\r\nthe computational function of lamina-specific synaptic connection\r\npatterns in stereotypical cortical microcircuits.We approach this problem by studying ...\r\nthe dynamical system defined by more realistic cortical microcircuit\r\nmodels as a whole and by investigating the influence that its\r\nlaminar structure has on the transmission and fusion of information\r\nwithin this dynamical system. The circuit models that we examine\r\nconsist of Hodgkin--Huxley neurons with dynamic synapses... We investigate to what\r\nextent this cortical microcircuit template supports the accumulation\r\nand fusion of information contained in generic spike inputs into\r\nlayer 4 and layers 2/3 and how well it makes this information\r\naccessible to projection neurons in layers 2/3 and layer 5. ... We conclude that computer simulations\r\nof detailed lamina-specific cortical microcircuit models\r\nprovide new insight into computational consequences of anatomical\r\nand physiological data. See paper for more and details.", - "tags": [ - { - "id": 810, - "tag": "CSIM (web link to model)" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 1114, - "tag": "ModelDB:82385" - } - ], - "timestamp_created": "2024-01-11 15:23:50.464063+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/82385", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "644": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 644, - "name": "Computational aspects of feedback in neural circuits (Maass et al 2006)", - "repository_type": "github", - "summary": "It had previously been shown that generic cortical microcircuit models\r\ncan perform complex real-time computations on continuous input\r\nstreams, provided that these computations can be carried out with a\r\nrapidly fading memory. We investigate ... the computational\r\ncapability of such circuits in the more realistic case where not only\r\nreadout neurons, but in addition a few neurons within the circuit have\r\nbeen trained for specific tasks. This is essentially equivalent to\r\nthe case where the output of trained readout neurons is fed back into\r\nthe circuit. We show that this new model overcomes the limitation of\r\na rapidly fading memory. In fact, we prove that in the idealized case\r\nwithout noise it can carry out any conceiv- able digital or analog\r\ncomputation on time-varying inputs. But even with noise the resulting\r\ncomputational model can perform a large class of biologically relevant\r\nreal-time computations that require a non-fading memory. ... In\r\nparticular we show that ... generic cortical microcircuits with\r\nfeedback provide a new model for working memory that is consistent\r\nwith a large set of biological constraints. See paper for more and details.", - "tags": [ - { - "id": 810, - "tag": "CSIM (web link to model)" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 1115, - "tag": "ModelDB:82392" - } - ], - "timestamp_created": "2024-01-11 15:23:50.981268+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/82392", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "645": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 645, - "name": "High dimensional dynamics and low dimensional readouts in neural microcircuits (Haeusler et al 2006)", - "repository_type": "github", - "summary": "We investigate generic models for cortical microcircuits,\r\ni.e. recurrent circuits of integrate-and fire neurons with dynamic\r\nsynapses. These complex dynamic systems subserve the amazing\r\ninformation processing capabilities of the cortex, but are at the\r\npresent time very little understood. We analyze the transient dynamics\r\nof models for neural microcircuits from the point of view of one or\r\ntwo readout neurons that collapse the high dimensional transient\r\ndynamics of a neural circuit into a 1- or 2--dimensional output\r\nstream. See paper for more and details.\r\n", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 718, - "tag": "Attractor Neural Network" - }, - { - "id": 810, - "tag": "CSIM (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 1116, - "tag": "ModelDB:82394" - } - ], - "timestamp_created": "2024-01-11 15:23:51.443888+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/82394", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "646": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 646, - "name": "Hysteresis in voltage gating of HCN channels (Elinder et al 2006, Mannikko et al 2005)", - "repository_type": "github", - "summary": "We found that HCN2 and HCN4 channels\r\nexpressed in oocytes from the frog Xenopus laevis do not display the\r\nactivation kinetic changes that we (previously) observed in spHCN and\r\nHCN1. However, HCN2 and HCN4 channels display changes in their tail\r\ncurrents, suggesting that these channels also undergo mode shifts and\r\nthat the conformational changes underlying the mode shifts are due to\r\nconserved aspects of HCN channels. With computer modelling, we show\r\nthat in channels with relatively slow opening kinetics and fast\r\nmode-shift transitions, such as HCN2 and HCN4 channels, the mode shift\r\neffects are not readily observable, except in the tail\r\nkinetics. Computer simulations of sino-atrial node action potentials\r\nsuggest that the HCN2 channel, together with the HCN1 channel, are\r\nimportant regulators of the heart firing frequency and that the mode\r\nshift is an important property to prevent arrhythmic firing. We\r\nconclude that although all HCN channels appear to undergo mode shifts\r\n\u2013 and thus may serve to prevent arrhythmic firing\r\n\u2013 it is mainly observable in ionic currents\r\nfrom HCN channels with faster kinetics. See papers for more and details.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 737, - "tag": "Heart disease" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 761, - "tag": "QBasic/QuickBasic/Turbo Basic/VBA" - }, - { - "id": 1117, - "tag": "ModelDB:82758" - } - ], - "timestamp_created": "2024-01-11 15:23:52.004498+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/82758", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "647": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 647, - "name": "Irregular spiking in NMDA-driven prefrontal cortex neurons (Durstewitz and Gabriel 2006)", - "repository_type": "github", - "summary": "Slow N-Methyl-D-aspartic acid (NMDA) synaptic currents are assumed to strongly contribute to the persistently elevated firing rates observed in prefrontal cortex (PFC) during working memory. During persistent activity, spiking of many neurons is highly irregular. ... The highest interspike-interval (ISI) variability occurred in a transition regime where the subthreshold membrane potential distribution shifts from mono- to bimodality, ... Predictability within irregular ISI series was significantly higher than expected from a noise-driven linear process, indicating that it might best be described through complex (potentially chaotic) nonlinear deterministic processes. Accordingly, the phenomena observed in vitro could be reproduced in purely deterministic biophysical model neurons. High spiking irregularity in these models emerged within a chaotic, close-to-bifurcation regime characterized by a shift of the membrane potential distribution from mono- to bimodality and by similar ISI return maps as observed in vitro. ... NMDA-induced irregular dynamics may have important implications for computational processes during working memory and neural coding.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 804, - "tag": "Bifurcation" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 794, - "tag": "Working memory" - }, - { - "id": 1118, - "tag": "ModelDB:82784" - } - ], - "timestamp_created": "2024-01-11 15:23:52.511319+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/82784", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "648": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 648, - "name": "Neocortical pyramidal neuron: deep; effects of dopamine (Durstewitz et al 2000)", - "repository_type": "github", - "summary": "\"... Simulated dopamine strongly enhanced high, delay-type activity but not low, spontaneous activity in the model network. Furthermore the strength of an afferent stimulation needed to disrupt delay-type activity increased with the magnitude of the dopamine-induced shifts in network parameters, making the currently active representation much more stable. Stability could be increased by dopamine-induced enhancements of the persistent Na(+) and N-methyl-D-aspartate (NMDA) conductances. Stability also was enhanced by a reduction in AMPA conductances. The increase in GABA(A) conductances that occurs after stimulation of dopaminergic D1 receptors was necessary in this context to prevent uncontrolled, spontaneous switches into high-activity states (i.e., spontaneous activation of task-irrelevant representations). In conclusion, the dopamine-induced changes in the biophysical properties of intrinsic ionic and synaptic conductances conjointly acted to highly increase stability of activated representations in PFC networks and at the same time retain control over network behavior and thus preserve its ability to adequately respond to task-related stimuli. ...\" See paper and references for more and details.", - "tags": [ - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1119, - "tag": "ModelDB:82849" - } - ], - "timestamp_created": "2024-01-11 15:23:53.028094+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/82849", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "649": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 649, - "name": "Distributed computing tool for NEURON, NEURONPM (screensaver) (Calin-Jageman and Katz 2006)", - "repository_type": "github", - "summary": "\"... To lower the barrier for large-scale model analysis, we have developedNeuronPM,\r\na client/server application that creates a \u201cscreen-saver\u201d cluster\r\nfor running simulations in NEURON (Hines & Carnevale, 1997). ... The NeuronPM client is\r\na Windows-based screen saver, and the NeuronPM server can be hosted\r\non any Apache/PHP/MySQL server. ... Administrative\r\npanels make it simple to upload model files, define the parameters and\r\nconditions to vary, and then monitor client status and work progress.\r\nNeuronPM is open-source freeware and is available for download at\r\nhttp://neuronpm.homeip.net. ...\"", - "tags": [ - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 811, - "tag": "NEURONPM (web link to tool)" - }, - { - "id": 1120, - "tag": "ModelDB:82891" - } - ], - "timestamp_created": "2024-01-11 15:23:53.489836+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/82891", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "650": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 650, - "name": "Networks of spiking neurons: a review of tools and strategies (Brette et al. 2007)", - "repository_type": "github", - "summary": "This package provides a series of codes that simulate networks of spiking neurons (excitatory and inhibitory, integrate-and-fire or Hodgkin-Huxley type, current-based or conductance-based synapses; some of them are event-based). The same networks are implemented in different simulators (NEURON, GENESIS, NEST, NCS, CSIM, XPP, SPLIT, MVAspike; there is also a couple of implementations in SciLab and C++).\r\nThe codes included in this package are benchmark simulations; see\r\nthe associated review paper (Brette et al. 2007). The \r\nmain goal is to provide a series of benchmark simulations of\r\nnetworks of spiking neurons, and demonstrate how these are implemented in the\r\ndifferent simulators overviewed in the paper. See also details in the\r\nenclosed file Appendix2.pdf, which describes these different \r\nbenchmarks. Some of these benchmarks were based on the \r\nVogels-Abbott model (Vogels TP and Abbott LF 2005).\r\n", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 645, - "tag": "Brian" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 812, - "tag": "CSIM" - }, - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 813, - "tag": "MVASpike" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 814, - "tag": "NCS" - }, - { - "id": 611, - "tag": "NEST" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 714, - "tag": "PyNN" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 815, - "tag": "SPLIT" - }, - { - "id": 816, - "tag": "SciLab" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1121, - "tag": "ModelDB:83319" - } - ], - "timestamp_created": "2024-01-11 15:23:54.017395+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/83319", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "651": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 651, - "name": "A simulation method for the firing sequences of motor units (Jiang et al 2006)", - "repository_type": "github", - "summary": "\" ... a novel model based on the Hodgkin\u2013Huxley (HH) system is proposed, which has the ability to simulate\r\nthe complex neurodynamics of the firing sequences of motor neurons. The model is presented at the cellular level and network level,\r\nand some simulation results from a simple 3-neuron network are presented to demonstrate its applications.\" See paper for more and details.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 1122, - "tag": "ModelDB:83320" - } - ], - "timestamp_created": "2024-01-11 15:23:54.605074+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/83320", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "652": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 652, - "name": "Pyramidal neuron coincidence detection tuned by dendritic branching pattern (Schaefer et al 2003)", - "repository_type": "github", - "summary": "\"... We examined the relationship between dendritic arborization\r\nand the coupling between somatic and dendritic action potential\r\n(AP) initiation sites in layer 5 (L5) neocortical pyramidal neurons.\r\nCoupling was defined as the relative reduction in threshold for\r\ninitiation of a dendritic calcium AP due to a coincident\r\nback-propagating AP. Simulations based on reconstructions of\r\nbiocytin-filled cells showed that addition of oblique branches of the\r\nmain apical dendrite in close proximity to the soma (d < 140 um)\r\nincreases the coupling between the apical and axosomatic AP initiation\r\nzones, whereas incorporation of distal branches decreases\r\ncoupling. ... We conclude that variation in dendritic arborization may\r\nbe a key determinant of variability in coupling (49+-17%; range\r\n19-83%; n = 37) and is likely to outweigh the contribution made by\r\nvariations in active membrane properties. Thus coincidence detection\r\nof inputs arriving from different cortical layers is strongly\r\nregulated by differences in dendritic arborization.\"", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 595, - "tag": "Coincidence Detection" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1123, - "tag": "ModelDB:83344" - } - ], - "timestamp_created": "2024-01-11 15:23:55.089373+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/83344", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "653": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 653, - "name": "Homosynaptic plasticity in the tail withdrawal circuit (TWC) of Aplysia (Baxter and Byrne 2006)", - "repository_type": "github", - "summary": "The tail-withdrawal circuit of Aplysia provides a useful model system for investigating synaptic dynamics. Sensory neurons within the circuit manifest several forms of synaptic plasticity. Here, we developed a model of the circuit and investigated the ways in which depression (DEP) and potentiation (POT) contributed to information processing. DEP limited the amount of motor neuron activity that could be elicited by the monosynaptic pathway alone. POT within the monosynaptic pathway did not compensate for DEP. There was, however, a synergistic interaction between POT and the polysynaptic pathway. This synergism extended the dynamic range of the network, and the interplay between DEP and POT made the circuit respond preferentially to long-duration, low-frequency inputs.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 722, - "tag": "Depression" - }, - { - "id": 723, - "tag": "Facilitation" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 760, - "tag": "Post-Tetanic Potentiation" - }, - { - "id": 757, - "tag": "SNNAP" - }, - { - "id": 758, - "tag": "Sensory processing" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 1124, - "tag": "ModelDB:83472" - } - ], - "timestamp_created": "2024-01-11 15:25:22.682252+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/83472", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "654": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 654, - "name": "INa and IKv4.3 heterogeneity in canine LV myocytes (Flaim et al 2006)", - "repository_type": "github", - "summary": "\"The roles of sustained components of INa and IKv43 in shaping the\r\naction potentials (AP) of myocytes isolated from the canine left\r\nventricle (LV) have not been studied in detail. Here we investigate\r\nthe hypothesis that these two currents can contribute substantially to\r\nheterogeneity of early repolarization and arrhythmic\r\nrisk.... The resulting simulations illustrate ways in which KChIP2- and\r\nCa2+- dependent control of IKv43 can result in a sustained outward\r\ncurrent that can neutralize INaL in a rate- and myocyte\r\nsubtype-dependent manner. Both these currents appear to play\r\nsignificant roles in modulating AP duration and rate dependence in\r\nmidmyocardial myocytes. ... By design, these models allow upward\r\nintegration into organ models or may be used as a basis for further\r\ninvestigations into cellular heterogeneities.\" See paper for more\r\nand details.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 737, - "tag": "Heart disease" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 817, - "tag": "I_Na,Ca" - }, - { - "id": 818, - "tag": "I_SERCA" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 819, - "tag": "Late Na" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 748, - "tag": "Markov-type model" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 738, - "tag": "Na/Ca exchanger" - }, - { - "id": 740, - "tag": "Na/K pump" - }, - { - "id": 741, - "tag": "Sodium pump" - }, - { - "id": 1125, - "tag": "ModelDB:83491" - } - ], - "timestamp_created": "2024-01-11 15:25:23.268454+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/83491", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "655": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 655, - "name": "Contibutions of input and history to motoneuron output (Powers et al 2005)", - "repository_type": "github", - "summary": "\"The present study presents results based\r\non recordings of noise-driven discharge in rat hypoglossal motoneurones ... First, we show that the hyperpolarizing trough is larger in Average Current Trajectories (ACTs)\r\ncalculated from spikes preceded by long interspike intervals, and minimal or absent in those\r\nbased on short interspike intervals. Second, we show that the trough is present for ACTs\r\ncalculated from the discharge of a threshold-crossing neurone model with a postspike after-\r\nhyperpolarization (AHP), but absent from those calculated from the discharge of a model\r\nwithout an AHP. We show that it is possible to represent noise-driven discharge using a\r\ntwo-component linear model that predicts discharge probability based on the sum of a feedback\r\nkernel and a stimulus kernel. The feedback kernel reflects the influence of prior discharge\r\nmediated by the AHP, and it increases in amplitude when AHP amplitude is increased by\r\npharmacological manipulations. Finally, we show that the predictions of this model are virtually\r\nidentical to those based on the first-order Wiener kernel. This suggests that the Wiener kernel\r\nderived from standard white-noise analysis of noise-driven discharge in neurones actually\r\nreflect the effects of both stimulus and discharge history.\" See paper for more and details.", - "tags": [ - { - "id": 820, - "tag": "IGOR Pro" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 1126, - "tag": "ModelDB:83508" - } - ], - "timestamp_created": "2024-01-11 15:25:23.736536+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/83508", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "656": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 656, - "name": "Prefrontal cortical mechanisms for goal-directed behavior (Hasselmo 2005)", - "repository_type": "github", - "summary": "\".. a model of prefrontal cortex function emphasizing\r\nthe influence of goal-related activity on the choice\r\nof the next motor output. ...\r\nDifferent neocortical minicolumns represent distinct sensory\r\ninput states and distinct motor output actions. The dynamics\r\nof each minicolumn include separate phases of encoding and\r\nretrieval. During encoding, strengthening of excitatory connections\r\nforms forward and reverse associations between each\r\nstate, the following action, and a subsequent state, which\r\nmay include reward. During retrieval, activity spreads from\r\nreward states throughout the network. The interaction of this\r\nspreading activity with a specific input state directs selection of\r\nthe next appropriate action. Simulations demonstrate how\r\nthese mechanisms can guide performance in a range of goal directed\r\ntasks, and provide a functional framework for some\r\nof the neuronal responses previously observed in the medial\r\nprefrontal cortex during performance of spatial memory tasks\r\nin rats.\"", - "tags": [ - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 809, - "tag": "Reinforcement Learning" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 1127, - "tag": "ModelDB:83512" - } - ], - "timestamp_created": "2024-01-11 15:25:24.294702+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/83512", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "657": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 657, - "name": "A reinforcement learning example (Sutton and Barto 1998)", - "repository_type": "github", - "summary": "This MATLAB script demonstrates an example of reinforcement learning\r\nfunctions guiding the movements of an agent (a black square) in a\r\ngridworld environment. See at the top of the matlab script and the book for more details.\r\n", - "tags": [ - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 809, - "tag": "Reinforcement Learning" - }, - { - "id": 1128, - "tag": "ModelDB:83514" - } - ], - "timestamp_created": "2024-01-11 15:25:24.761154+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/83514", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "658": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 658, - "name": "Hippocampal context-dependent retrieval (Hasselmo and Eichenbaum 2005)", - "repository_type": "github", - "summary": "\"... The model simulates the context-sensitive firing properties of hippocampal neurons including trial-specific firing during spatial\r\nalternation and trial by trial changes in theta phase precession on a linear track. ...\" See paper for more and details.", - "tags": [ - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 809, - "tag": "Reinforcement Learning" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 1129, - "tag": "ModelDB:83516" - } - ], - "timestamp_created": "2024-01-11 15:25:25.278037+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/83516", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "659": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 659, - "name": "Fixed point attractor (Hasselmo et al 1995)", - "repository_type": "github", - "summary": "\"... In the model, cholinergic suppression of synaptic transmission at excitatory feedback synapses is shown to determine the extent to which activity depends upon new features of the afferent input versus components of previously stored representations. ...\" See paper for more and details. The MATLAB script demonstrates the model of fixed point attractors mediated by excitatory feedback with subtractive inhibition in a continuous firing rate model.\r\n", - "tags": [ - { - "id": 718, - "tag": "Attractor Neural Network" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 809, - "tag": "Reinforcement Learning" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 1130, - "tag": "ModelDB:83517" - } - ], - "timestamp_created": "2024-01-11 15:25:26.081765+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/83517", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "660": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 660, - "name": "Ambiguous Encoding and Distorted Perception (Carlson and Kawasaki 2006)", - "repository_type": "github", - "summary": "\"... In\r\nthe weakly electric fish Eigenmannia, P- and T-type primary afferent fibers are specialized for encoding the amplitude and phase,\r\nrespectively, of electrosensory stimuli. We used a stimulus estimation technique to quantify the ability of P- and T-units to encode\r\nrandom modulations in amplitude and phase. As expected, P-units exhibited a clear preference for encoding amplitude modulations,\r\nwhereas T-units exhibited a clear preference for encoding phase modulations. Surprisingly, both types of afferents also encoded their\r\nnonpreferred stimulus attribute when it was presented in isolation or when the preferred stimulus attribute was sufficiently weak.\r\nBecause afferent activity can be affected by modulations in either amplitude or phase, it is not possible to unambiguously distinguish\r\nbetween these two stimulus attributes by observing the activity of a single afferent fiber. Simple model neurons with a preference for\r\nencoding either amplitude or phase also encoded their nonpreferred stimulus attribute when it was presented in isolation, suggesting that\r\nsuch ambiguity is unavoidable. ... \" See paper for more and details.", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 821, - "tag": "Sensory coding" - }, - { - "id": 1131, - "tag": "ModelDB:83520" - } - ], - "timestamp_created": "2024-01-11 15:25:26.614386+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/83520", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "661": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 661, - "name": "Combination sensitivity and active conductances (Carlson and Kawasaki 2006)", - "repository_type": "github", - "summary": "\"... The weakly electric fish Gymnarchus discriminates the\r\nsign of the frequency difference (Df) between a neighbor\u2019s electric\r\norgan discharge (EOD) and its own EOD by comparing temporal\r\npatterns of amplitude modulation (AM) and phase modulation (PM).\r\nSign-selective neurons in the midbrain respond preferentially to either\r\npositive frequency differences (Df >0 selective) or negative frequency\r\ndifferences (Df <0 selective). To study the mechanisms of\r\ncombination sensitivity, we made whole cell intracellular recordings\r\nfrom sign-selective midbrain neurons in vivo and recorded postsynaptic\r\npotential (PSP) responses to AM, PM, Df >0, and Df <0.\r\n... Responses to the nonpreferred\r\nsign of Df, but not the preferred sign of Df, were substantially weaker\r\nthan linear predictions, causing a significant increase in the actual\r\ndegree of sign selectivity. By using various levels of current clamp\r\nand comparing our results to simple models of synaptic integration,\r\nwe demonstrate that this decreased response to the nonpreferred sign\r\nof Df is caused by a reduction in voltage-dependent excitatory\r\nconductances. This finding reveals that nonlinear decoders, in the\r\nform of voltage-dependent conductances, can enhance the selectivity\r\nof single neurons for particular combinations of stimulus attributes.\" See paper for more and details.", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 1132, - "tag": "ModelDB:83521" - } - ], - "timestamp_created": "2024-01-11 15:25:27.094532+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/83521", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "662": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 662, - "name": "Subthreshold inact. of K channels modulates APs in bitufted interneurons (Korngreen et al 2005)", - "repository_type": "github", - "summary": "... In this study we show that in bitufted interneurones\r\nfrom layer 2/3 of the somatosensory cortex, the height and width of APs recorded at the\r\nsoma are sensitive to changes in the resting membrane potential, suggesting subthreshold\r\nactivity of voltage-gated conductances. Attributes of K+ currents examined in nucleated\r\npatches revealed a fast subthreshold-inactivating K+ conductance (Kf ) and a slow\r\nsuprathreshold-inactivating K+ conductance (Ks ). Simulations of these K+ conductances,\r\nincorporated into a Hodgkin\u2013Huxley-type model, suggested that during a single AP or during\r\nlow frequency trains of APs, subthreshold inactivation of Kf was the primary modulator of AP\r\nshape, whereas during trains of APs the shape was governed to a larger degree by Ks resulting\r\nin the generation of smaller and broader APs. ... Compartmental simulation\r\nof the back-propagating AP suggested a mechanism for the modulation of the back-propagating\r\nAP height and width by subthreshold activation of Kf . We speculate that this signal may\r\nmodulate retrograde GABA release and consequently depression of synaptic efficacy of excitatory\r\ninput from neighbouring pyramidal neurones.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1133, - "tag": "ModelDB:83523" - } - ], - "timestamp_created": "2024-01-11 15:25:27.573455+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/83523", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "663": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 663, - "name": "Cortex learning models (Weber at al. 2006, Weber and Triesch, 2006, Weber and Wermter 2006/7)", - "repository_type": "github", - "summary": "A simulator and the configuration files for three publications are\r\nprovided. First, \"A hybrid generative and predictive model of the motor\r\ncortex\" (Weber at al. 2006) which uses reinforcement learning to set up a\r\ntoy action scheme, then uses unsupervised learning to \"copy\" the learnt\r\naction, and an attractor network to predict the hidden code of the\r\nunsupervised network. Second, \"A Self-Organizing Map of Sigma-Pi Units\"\r\n(Weber and Wermter 2006/7) learns frame of reference transformations on\r\npopulation codes in an unsupervised manner. Third, \"A possible\r\nrepresentation of reward in the learning of saccades\" (Weber and Triesch,\r\n2006) implements saccade learning with two possible learning schemes for\r\nhorizontal and vertical saccades, respectively.", - "tags": [ - { - "id": 718, - "tag": "Attractor Neural Network" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 808, - "tag": "Hebbian plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 791, - "tag": "Rate-coding model neurons" - }, - { - "id": 809, - "tag": "Reinforcement Learning" - }, - { - "id": 803, - "tag": "Unsupervised Learning" - }, - { - "id": 822, - "tag": "Winner-take-all" - }, - { - "id": 1134, - "tag": "ModelDB:83528" - } - ], - "timestamp_created": "2024-01-11 15:25:28.045294+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/83528", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "664": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 664, - "name": "Regulation of the firing pattern in dopamine neurons (Komendantov et al 2004)", - "repository_type": "github", - "summary": "Midbrain dopaminergic (DA) neurons in vivo exhibit two major firing patterns: single-spike firing and burst firing. The firing pattern expressed is dependent on both the intrinsic properties of the neurons and their excitatory and inhibitory synaptic inputs. Experimental data suggest that the activation of NMDA and GABAA receptors is crucial contributor to the initiation and suppression of burst firing, respectively, and that blocking calcium-activated potassium channels can facilitate burst firing. This multi-compartmental model of a DA neuron with a branching structure was developed and calibrated based on in vitro experimental data to explore the effects of different levels of activation of NMDA and GABAA receptors as well as the modulation of the SK current on the firing activity.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 740, - "tag": "Na/K pump" - }, - { - "id": 741, - "tag": "Sodium pump" - }, - { - "id": 1135, - "tag": "ModelDB:83547" - } - ], - "timestamp_created": "2024-01-11 15:25:28.594563+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/83547", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "665": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 665, - "name": "Role of active dendrites in rhythmically-firing neurons (Goldberg et al 2006)", - "repository_type": "github", - "summary": "\"The responsiveness of rhythmically-firing neurons to synaptic inputs is characterized by their phase response curve (PRC), which relates how weak somatic perturbations affect the timing of the next action potential. The shape of the somatic PRC is an important determinant of collective network dynamics. Here we study theoretically and experimentally the impact of distally-located synapses and dendritic nonlinearities on the synchronization properties of rhythmically firing neurons. Combining the theories of quasi-active cables and phase-coupled oscillators we derive an approximation for the dendritic responsiveness, captured by the neuron's dendritic PRC (dPRC). This closed-form expression indicates that the dPRCs are linearly-filtered versions of the somatic PRC, and that the filter characteristics are determined by the passive and active properties of the dendrite. ... collective dynamics can be qualitatively different depending on the location of the synapse, the neuronal firing rates and the dendritic nonlinearities.\" See paper for more and details.", - "tags": [ - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 823, - "tag": "Phase Response Curves" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1136, - "tag": "ModelDB:83558" - } - ], - "timestamp_created": "2024-01-11 15:25:29.262798+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/83558", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "666": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 666, - "name": "Spiking neuron model of the basal ganglia (Humphries et al 2006)", - "repository_type": "github", - "summary": "A spiking neuron model of the basal ganglia (BG) circuit (striatum, STN, GP, SNr). Includes: parallel anatomical channels; tonic dopamine; dopamine receptors in striatum, STN, and GP; burst-firing in STN; GABAa, AMPA, and NMDA currents; effects of synaptic location. Model demonstrates selection and switching of input signals. Replicates experimental data on changes in slow-wave (<1 Hz) and gamma-band oscillations within BG nuclei following lesions and pharmacological manipulations.", - "tags": [ - { - "id": 771, - "tag": "Action Selection/Decision Making" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 573, - "tag": "Rebound firing" - }, - { - "id": 592, - "tag": "Sleep" - }, - { - "id": 1137, - "tag": "ModelDB:83559" - } - ], - "timestamp_created": "2024-01-11 15:25:29.965236+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/83559", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "667": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 667, - "name": "Population-level model of the basal ganglia and action selection (Gurney et al 2001, 2004)", - "repository_type": "github", - "summary": "We proposed a new functional architecture for the basal ganglia (BG) based on the premise that these brain structures play a central role in behavioural action selection. The papers quantitatively describes the properties of the model using analysis and simulation. In the first paper, we show that the decomposition of the BG into selection and control pathways is supported in several ways. First, several elegant features are exposed--capacity scaling, enhanced selectivity and synergistic dopamine modulation--which might be expected to exist in a well designed action selection mechanism. Second, good matches between model GPe output and GPi and SNr output, and neurophysiological data, have been found. Third, the behaviour of the model as a signal selection mechanism has parallels with some kinds of action selection observed in animals under various levels of dopaminergic modulation.\r\n\r\nIn the second paper, we extend the BG model to include new connections, and show that action selection is maintained. In addition, we provide quantitative measures for defining different forms of selection, and methods for assessing performance changes in computational neuroscience models. \r\n\r\n\r\n", - "tags": [ - { - "id": 771, - "tag": "Action Selection/Decision Making" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 824, - "tag": "Simulink" - }, - { - "id": 1138, - "tag": "ModelDB:83560" - } - ], - "timestamp_created": "2024-01-11 15:25:30.509376+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/83560", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "668": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 668, - "name": "Basal ganglia-thalamocortical loop model of action selection (Humphries and Gurney 2002)", - "repository_type": "github", - "summary": "We embed our basal ganglia model into a wider circuit containing the motor thalamocortical loop and thalamic reticular nucleus (TRN). Simulation of this extended model showed that the additions gave five main results which are desirable in a selection/switching mechanism. First, low salience actions (i.e. those with low urgency) could be selected. Second, the range of salience values over which actions could be switched between was increased. Third, the contrast between the selected and non-selected actions was enhanced via improved differentiation of outputs from the BG. Fourth, transient increases in the salience of a non-selected action were prevented from interrupting the ongoing action, unless the transient was of sufficient magnitude. Finally, the selection of the ongoing action persisted when a new closely matched salience action became active. The first result was facilitated by the thalamocortical loop; the rest were dependent on the presence of the TRN. Thus, we conclude that the results are consistent with these structures having clearly defined functions in action selection.", - "tags": [ - { - "id": 771, - "tag": "Action Selection/Decision Making" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 824, - "tag": "Simulink" - }, - { - "id": 1139, - "tag": "ModelDB:83562" - } - ], - "timestamp_created": "2024-01-11 15:25:31.045034+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/83562", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "669": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 669, - "name": "A theory of ongoing activity in V1 (Goldberg et al 2004)", - "repository_type": "github", - "summary": "Ongoing spontaneous activity in the cerebral cortex exhibits\r\ncomplex spatiotemporal patterns in the absence of sensory stimuli. To elucidate the nature of\r\nthis ongoing activity, we present a theoretical treatment of two contrasting scenarios of cortical dynamics: (1) fluctuations about a single background state\r\nand (2) wandering among multiple \u201cattractor\u201d states, which\r\nencode a single or several stimulus features.\r\nStudying simplified network rate models of the primary\r\nvisual cortex (V1), we show that the single state scenario\r\nis characterized by fast and high-dimensional\r\nGaussian-like fluctuations, whereas in the multiple\r\nstate scenario the fluctuations are slow, low dimensional,\r\nand highly non-Gaussian. Studying a more realistic model that incorporates correlations in the feedforward input, spatially restricted cortical interactions,\r\nand an experimentally derived layout of pinwheels,\r\nwe show that recent optical-imaging data of ongoing\r\nactivity in V1 are consistent with the presence of either\r\na single background state or multiple attractor states\r\nencoding many features.", - "tags": [ - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 791, - "tag": "Rate-coding model neurons" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1140, - "tag": "ModelDB:83570" - } - ], - "timestamp_created": "2024-01-11 15:25:31.561425+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/83570", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "670": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 670, - "name": "Modeling interactions in Aplysia neuron R15 (Yu et al 2004)", - "repository_type": "github", - "summary": "\"The biophysical properties of neuron R15 in Aplysia endow it with the ability to express multiple modes of oscillatory electrical activity, such as beating and bursting. Previous modeling studies examined the ways in which membrane conductances contribute to the electrical activity of R15 and the ways in which extrinsic modulatory inputs alter the membrane conductances by biochemical cascades and influence the electrical activity. The goals of the present study were to examine the ways in which electrical activity influences the biochemical cascades and what dynamical properties emerge from the ongoing interactions between electrical activity and these cascades.\" See paper for more and details.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1141, - "tag": "ModelDB:83575" - } - ], - "timestamp_created": "2024-01-11 15:25:32.098583+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/83575", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "671": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 671, - "name": "Input Fluctuations effects on f-I curves (Arsiero et al. 2007)", - "repository_type": "github", - "summary": "\"... We examined in vitro frequency versus current (f-I) relationships of layer 5 (L5) pyramidal cells of the rat medial prefrontal cortex (mPFC) using fluctuating stimuli. ...our results show that mPFC L5 pyramidal neurons retain an increased sensitivity to input fluctuations, whereas their sensitivity to the input mean diminishes to near zero. This implies that the discharge properties of L5 mPFC neurons are well suited to encode input fluctuations rather than input mean in their firing rates, with important consequences for information processing and stability of persistent activity at the network level.\"", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 777, - "tag": "Spike Frequency Adaptation" - }, - { - "id": 1142, - "tag": "ModelDB:83590" - } - ], - "timestamp_created": "2024-01-11 15:25:32.592636+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/83590", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "672": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 672, - "name": "Linking STDP and Dopamine action to solve the distal reward problem (Izhikevich 2007)", - "repository_type": "github", - "summary": "\"... How does the brain know what firing patterns of what neurons are responsible for the reward if 1) the patterns are no longer there when the reward arrives and 2) all\r\nneurons and synapses are active during the waiting period to the\r\nreward? Here, we show how the conundrum is resolved by a model network\r\nof cortical spiking neurons with spike-timing-dependent plasticity\r\n(STDP) modulated by dopamine (DA). Although STDP is triggered by\r\nnearly coincident firing patterns on a millisecond timescale, slow\r\nkinetics of subsequent synaptic plasticity is sensitive to changes in\r\nthe extracellular DA concentration during the critical period of a few\r\nseconds. ... This study emphasizes the importance of precise firing\r\npatterns in brain dynamics and suggests how a global diffusive\r\nreinforcement signal in the form of extracellular DA can selectively\r\ninfluence the right synapses at the right time.\" See paper for more and details.", - "tags": [ - { - "id": 825, - "tag": "Learning" - }, - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 802, - "tag": "STDP" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 1143, - "tag": "ModelDB:84167" - } - ], - "timestamp_created": "2024-01-11 15:25:33.057561+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/84167", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "673": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 673, - "name": "Extracellular Action Potential Simulations (Gold et al 2007)", - "repository_type": "github", - "summary": "This package recreates the the principal experiments described in (Gold, Henze and Koch, 2007) and includes the core code necessary to create your own Extracellular Action Potential Simulations.", - "tags": [ - { - "id": 734, - "tag": "Extracellular Fields" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1144, - "tag": "ModelDB:84589" - } - ], - "timestamp_created": "2024-01-11 15:25:33.571872+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/84589", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "674": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 674, - "name": "Temporal decorrelation by intrinsic cellular dynamics (Wang et al 2003)", - "repository_type": "github", - "summary": "\"... Recent investigations in primary visual (V1) cortical neurons have\r\ndemonstrated that adaptation to prolonged changes in stimulus contrast\r\nis mediated in part through intrinsic ionic currents, a Ca2+\r\nactivated K+ current (IKCa) and especially a Na+ activated K+ current\r\n(IKNa). The present study was designed to test the hypothesis that\r\nthe activation of adaptation ionic currents may provide a cellular\r\nmechanism for temporal decorrelation in V1. A conductance-based\r\nneuron model was simulated, which included an IKCa and an IKNa. We\r\nshow that the model neuron reproduces the adaptive behavior of V1\r\nneurons in response to high contrast inputs. ...\". See paper for details and more.", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 726, - "tag": "Vision" - }, - { - "id": 1145, - "tag": "ModelDB:84593" - } - ], - "timestamp_created": "2024-01-11 15:25:34.040561+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/84593", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "675": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 675, - "name": "Synaptic integration of an identified nonspiking interneuron in crayfish (Takashima et al 2006)", - "repository_type": "github", - "summary": "This GENESIS simulation shows how a single or compound excitatory synaptic potential evoked by mechanosensory stimulation spreads over the dendrites of the LDS interneuron that is one of the identified nonspiking interneurons in the central nervous system of crayfish Procambarus clarkii. The model is based on physiological experiments carried out by Akira Takashima using single-electrode voltage clamp techniques and also 3-D morphometry of the interneuron carried out by Ryou Hikosaka using confocal laser scanning microscopic techniques. The physiological and morphological studies were coordinated by Masakazu Takahata.", - "tags": [ - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 1146, - "tag": "ModelDB:84599" - } - ], - "timestamp_created": "2024-01-11 15:25:34.611145+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/84599", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "676": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 676, - "name": "Zonisamide-induced inhibition of the firing of APs in hippocampal neurons (Huang et al. 2007)", - "repository_type": "github", - "summary": "Zonisamide (ZNS), a synthetic benzisoxazole derivative, has been used as an alternative choice in the treatment of epilepsy with a better efficacy and safety profile. However, little is known regarding the mechanism of ZNS actions on ion currents in neurons. We thus investigated its effect on ion currents in differentiated hippocampal 19-7 cells. The ZNS (30 uM) reversibly increased the amplitude of K+ outward currents and paxilline (1 uM) was effective in suppressing ZNS-induced increase of K+ outward currents. In inside-out configuration, ZNS (30 uM) applied to the intracellular face of the membrane did not alter single-channel conductance; however, it did enhance the activity of large-conductance Ca2+-activated K+ (BKCa) channels primarily by decreasing mean closed time. The EC50 value for ZNS-stimulated BKCa channels was 34 uM. This drug caused a left shift in the activation curve of BKCa channels with no change in the gating charge of these channels. ZNS at a concentration greater than 100 uM also reduced the amplitude of A-type K+ current in these cells. A simulation modeling based on hippocampal CA3 pyramidal neurons (Pinsky-Rinzel model) was also analyzed to investigate the inhibitory effect of ZNS on the firing of simulated action potentials. Taken together, this study suggests that in hippocampal neurons, during the exposure to ZNS, the ZNS-mediated effects on BKCa channels and IA could be one of the ionic mechanisms through which it affects neuronal excitability.", - "tags": [ - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1147, - "tag": "ModelDB:84606" - } - ], - "timestamp_created": "2024-01-11 15:25:35.093961+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/84606", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "677": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 677, - "name": "Differential modulation of pattern and rate in a dopamine neuron model (Canavier and Landry 2006)", - "repository_type": "github", - "summary": "\"A stylized, symmetric, compartmental model of a dopamine neuron in vivo shows how rate and pattern can be modulated either concurrently or differentially. If two or more parameters in the model are varied concurrently, the baseline firing rate and the extent of bursting become decorrelated, which provides an explanation for the lack of a tight correlation in vivo and is consistent with some independence of the mechanisms that generate baseline firing rates versus bursting. ...\" See paper for more and details.\r\n", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 763, - "tag": "Intrinsic plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 740, - "tag": "Na/K pump" - }, - { - "id": 741, - "tag": "Sodium pump" - }, - { - "id": 1148, - "tag": "ModelDB:84612" - } - ], - "timestamp_created": "2024-01-11 15:25:35.587828+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/84612", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "678": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 678, - "name": "Method for counting motor units in mice (Major et al 2007)", - "repository_type": "github", - "summary": "\"... Our goal was to develop an efficient method\r\nto determine the number of motor neurons making functional connections\r\nto muscle in a transgenic mouse model of amyotrophic lateral\r\nsclerosis (ALS). We developed a novel protocol for motor unit\r\nnumber estimation (MUNE) using incremental stimulation. The\r\nmethod involves analysis of twitch waveforms using a new software\r\nprogram, ITS-MUNE, designed for interactive calculation of motor\r\nunit number. The method was validated by testing simulated twitch\r\ndata from a mathematical model of the neuromuscular system. Computer\r\nsimulations followed the same stimulus-response protocol and\r\nproduced waveform data that were indistinguishable from experiments.\r\n... The ITS-MUNE analysis method has the potential to quantitatively\r\nmeasure the progression of motor neuron diseases and therefore the\r\nefficacy of treatments designed to alleviate pathologic processes of\r\nmuscle denervation.\" The software is available for download under the \"ITS-MUNE software\" link at \r\n(see below for links).\"", - "tags": [ - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 787, - "tag": "Pathophysiology" - }, - { - "id": 1149, - "tag": "ModelDB:84627" - } - ], - "timestamp_created": "2024-01-11 15:25:36.073485+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/84627", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "679": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 679, - "name": "A cardiac cell simulator (Puglisi and Bers 2001), applied to the QT interval (Busjahn et al 2004)", - "repository_type": "github", - "summary": "\"LabHEART is an easy to use program that simulates the cardiac action potential, calcium transient and ionic currents. Key parameters such as ionic concentration, stimulus waveform and channel conductance can easily be changed by a click on an icon or dragging a slider.\r\n\r\nIt is a powerfull tool for teaching and researching cardiac electrophysiology.\"", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 737, - "tag": "Heart disease" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 732, - "tag": "Tutorial/Teaching" - }, - { - "id": 1150, - "tag": "ModelDB:84641" - } - ], - "timestamp_created": "2024-01-11 15:25:36.605343+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/84641", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "680": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 680, - "name": "Squid axon (Hodgkin, Huxley 1952) (LabAXON)", - "repository_type": "github", - "summary": "The classic HH model of squid axon membrane implemented in LabAXON. Hodgkin, A.L., Huxley, A.F. (1952)", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1151, - "tag": "ModelDB:84649" - } - ], - "timestamp_created": "2024-01-11 15:25:37.153253+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/84649", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "681": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 681, - "name": "Recording from rod bipolar axon terminals in situ (Oltedal et al 2007)", - "repository_type": "github", - "summary": "\"... Whole cell\r\nrecordings from axon terminals and cell bodies were used to investigate\r\nthe passive membrane properties of rod bipolar cells and analyzed\r\nwith a two-compartment equivalent electrical circuit model\r\ndeveloped by Mennerick et al. For both terminal- and soma-end\r\nrecordings, capacitive current decays were well fitted by biexponential\r\nfunctions. Computer simulations of simplified models of rod bipolar\r\ncells demonstrated that estimates of the capacitance of the axon\r\nterminal compartment can depend critically on the recording location,\r\nwith terminal-end recordings giving the best estimates. Computer\r\nsimulations and whole cell recordings demonstrated that terminal-end\r\nrecordings can yield more accurate estimates of the peak amplitude\r\nand kinetic properties of postsynaptic currents generated at the axon\r\nterminals due to increased electrotonic filtering of these currents when\r\nrecorded at the soma. ...\" See paper for more and details.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1152, - "tag": "ModelDB:84655" - } - ], - "timestamp_created": "2024-01-11 15:25:37.742691+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/84655", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "682": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 682, - "name": "Models of Na channels from a paper on the PKC control of I Na,P (Baker 2005)", - "repository_type": "github", - "summary": "\"The tetrodotoxin-resistant (TTX-r) persistent Na(+) current, attributed to Na(V)1.9, was recorded in small (< 25 mum apparent diameter) dorsal root ganglion (DRG) neurones cultured from P21 rats and from adult wild-type and Na(V)1.8 null mice. ... Numerical simulation of the up-regulation qualitatively reproduced changes in sensory neurone firing properties. ...\" Note: models of NaV1.8 and NaV1.9 and also persistent and transient Na channels that collectively model Nav 1.1, 1.6, and 1.7 are present in this model.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 775, - "tag": "Nociception" - }, - { - "id": 751, - "tag": "Signaling pathways" - }, - { - "id": 1153, - "tag": "ModelDB:85112" - } - ], - "timestamp_created": "2024-01-11 15:25:38.391628+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/85112", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "683": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 683, - "name": "Modeling temperature changes in AMPAR kinetics (Postlethwaite et al 2007)", - "repository_type": "github", - "summary": "This model was used to simulate glutamatergic, AMPA receptor mediated mEPSCs (miniature EPSCs, resulting from spontaneous vesicular transmitter release) at the calyx of Held synapse. It was used to assess the influence of temperature (physiological vs. subphysiological) on the amplitude and time course of mEPSCs. In the related paper, simulation results were directly compared to the experimental data, and it was concluded that an increase of temperature accelerates AMPA receptor kinetics.", - "tags": [ - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 826, - "tag": "MCell" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - }, - { - "id": 1154, - "tag": "ModelDB:85981" - } - ], - "timestamp_created": "2024-01-11 15:25:39.000269+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/85981", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "684": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 684, - "name": "TTX-R Na+ current effect on cell response (Herzog et al 2001)", - "repository_type": "github", - "summary": "\"Small dorsal root ganglion (DRG) neurons, which include nociceptors,\r\nexpress multiple voltage-gated sodium currents. In addition to a\r\nclassical fast inactivating tetrodotoxin-sensitive (TTX-S) sodium\r\ncurrent, many of these cells express a TTX-resistant (TTX-R) sodium\r\ncurrent that activates near -70 mV and is persistent at negative\r\npotentials. To investigate the possible contributions of this TTX-R\r\npersistent (TTX-RP) current to neuronal excitability, we carried out\r\ncomputer simulations using the Neuron program with TTX-S and -RP\r\ncurrents, fit by the Hodgkin-Huxley model, that closely matched the\r\ncurrents recorded from small DRG neurons. ...\" See paper for more and details.", - "tags": [ - { - "id": 576, - "tag": "I K" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 775, - "tag": "Nociception" - }, - { - "id": 1155, - "tag": "ModelDB:86537" - } - ], - "timestamp_created": "2024-01-11 15:25:39.483704+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/86537", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "685": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 685, - "name": "Model of calcium oscillations in olfactory cilia (Reidl et al. 2006)", - "repository_type": "github", - "summary": "Simulation of experiments on olfactory receptor neurons (ORNs). Focussing on the negative feedback that calcium (through calmodulin) has on its own influx through CNG channels, this model is able to reproduce both calcium oscillations as well as adaptation behaviour as seen in experiments done with ORNs.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 766, - "tag": "I CNG" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 751, - "tag": "Signaling pathways" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 1156, - "tag": "ModelDB:86538" - } - ], - "timestamp_created": "2024-01-11 15:25:39.990206+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/86538", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "686": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 686, - "name": "STDP and NMDAR Subunits (Gerkin et al. 2007)", - "repository_type": "github", - "summary": "The paper argues for competing roles of NR2A- and NR2B-containing NMDARs in spike-timing-dependent plasticity. This simple dynamical model recapitulates the results of STDP experiments involving selective blockers of NR2A- and NR2B-containing NMDARs, for which the stimuli are pre- and postsynaptic spikes in varying combinations. Experiments were done using paired recordings from glutamatergic neurons in rat hippocampal cultures. This model focuses on the dynamics of the putative potentiation and depression modules themselves, and their interaction For detailed dynamics involving NMDARs and Ca2+ transients, see Rubin et al., J. Neurophys., 2005.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 820, - "tag": "IGOR Pro" - }, - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 802, - "tag": "STDP" - }, - { - "id": 751, - "tag": "Signaling pathways" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 822, - "tag": "Winner-take-all" - }, - { - "id": 1157, - "tag": "ModelDB:87216" - } - ], - "timestamp_created": "2024-01-11 15:25:40.484500+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/87216", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "687": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 687, - "name": "Markov models of SCN1A (NaV1.1) applied to abnormal gating and epilepsy (Clancy and Kass 2004)", - "repository_type": "github", - "summary": "\"Recently, some forms of idiopathic epilepsy have been causally related to\r\ngenetic mutations in neuronal ion channels. To understand disease\r\nmechanisms, it is crucial to understand how a gene defect can disrupt\r\nchannel gating, which in turn can affect complex cellular dynamic\r\nprocesses. We develop a theoretical Markovian model of the neuronal\r\nNa+ channel NaV1.1 to explore and explain gating mechanisms underlying\r\ncellular excitability and physiological and pathophysiological\r\nmechanisms of abnormal neuronal excitability in the context of\r\nepilepsy. ...\"", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 748, - "tag": "Markov-type model" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1158, - "tag": "ModelDB:87278" - } - ], - "timestamp_created": "2024-01-11 15:25:41.073049+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/87278", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "688": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 688, - "name": "Amyloid beta (IA block) effects on a model CA1 pyramidal cell (Morse et al. 2010)", - "repository_type": "github", - "summary": "The model simulations provide evidence oblique dendrites in CA1 pyramidal neurons are susceptible to hyper-excitability by amyloid beta block of the transient K+ channel, IA. See paper for details.", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 827, - "tag": "Aging/Alzheimer`s" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 787, - "tag": "Pathophysiology" - }, - { - "id": 1159, - "tag": "ModelDB:87284" - } - ], - "timestamp_created": "2024-01-11 15:25:41.611895+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/87284", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "689": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 689, - "name": "Squid axon (Hodgkin, Huxley 1952) (SBML, XPP, other)", - "repository_type": "github", - "summary": "An SBML (and related XPP and other formats) implementation of the classic HH paper is available in the BIOMODELS database. See far below for links.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 828, - "tag": "SBML (web link to model)" - }, - { - "id": 829, - "tag": "XML (web link to model)" - }, - { - "id": 830, - "tag": "XPP (web link to model)" - }, - { - "id": 1160, - "tag": "ModelDB:87450" - } - ], - "timestamp_created": "2024-01-11 15:25:42.138202+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/87450", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "690": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 690, - "name": "Oscillating neurons in the cochlear nucleus (Bahmer Langner 2006a, b, and 2007)", - "repository_type": "github", - "summary": "\"Based on the physiological and anatomical data, we propose a model consisting of a minimum network of two choppers that are interconnected with a synaptic delay of 0.4 ms (Bahmer and Langner 2006a) . Such minimum delays have been found in different systems and in various animals (e.g. Hackett, Jackson, and Rubel 1982; Borst, Helmchen, and Sakmann 1995). The choppers receive input from both the auditory nerve and an onset neuron. This model can reproduce the mean, standard deviation, and coefficient of variation of the ISI and the dynamic features of AM coding of choppers.\"", - "tags": [ - { - "id": 762, - "tag": "Audition" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1161, - "tag": "ModelDB:87454" - } - ], - "timestamp_created": "2024-01-11 15:25:42.688372+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/87454", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "691": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 691, - "name": "AP shape and parameter constraints in optimization of compartment models (Weaver and Wearne 2006)", - "repository_type": "github", - "summary": "\"... We construct an\r\nobjective function that includes both time-aligned action potential shape error and errors in firing rate and firing regularity. We then\r\nimplement a variant of simulated annealing that introduces a recentering algorithm to handle infeasible points outside the boundary\r\nconstraints. We show how our objective function captures essential features of neuronal firing patterns, and why our boundary\r\nmanagement technique is superior to previous approaches.\"", - "tags": [ - { - "id": 590, - "tag": "I A" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 1162, - "tag": "ModelDB:87473" - } - ], - "timestamp_created": "2024-01-11 15:25:43.163171+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/87473", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "692": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 692, - "name": "CA1 pyramidal neurons: binding properties and the magical number 7 (Migliore et al. 2008)", - "repository_type": "github", - "summary": "NEURON files from the paper:\r\n\r\nSingle neuron binding properties and the magical number 7,\r\nby M. Migliore, G. Novara, D. Tegolo, Hippocampus, in press (2008).\r\n\r\nIn an extensive series of simulations with realistic morphologies and active properties, \r\nwe demonstrate how n radial (oblique) dendrites of these neurons may be used to bind n inputs \r\nto generate an output signal. \r\nThe results suggest a possible neural code as the most effective n-ple of dendrites that \r\ncan be used for short-term memory recollection of persons, objects, or places. \r\nOur analysis predicts a straightforward physiological explanation for the observed \r\npuzzling limit of about 7 short-term memory items that can be stored by humans.\r\n", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 771, - "tag": "Action Selection/Decision Making" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 595, - "tag": "Coincidence Detection" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 825, - "tag": "Learning" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 719, - "tag": "Pattern Recognition" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 794, - "tag": "Working memory" - }, - { - "id": 1163, - "tag": "ModelDB:87535" - } - ], - "timestamp_created": "2024-01-11 15:25:43.730305+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/87535", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "693": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 693, - "name": "CA1 oriens alveus interneurons: signaling properties (Minneci et al. 2007)", - "repository_type": "github", - "summary": "The model supports the experimental findings showing that the dynamic interaction between cells with various firing patterns could differently affect GABAergic signaling, leading to a wide range of interneuronal communication within the hippocampal network.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 751, - "tag": "Signaling pathways" - }, - { - "id": 1164, - "tag": "ModelDB:87546" - } - ], - "timestamp_created": "2024-01-11 15:25:44.240179+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/87546", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "694": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 694, - "name": "Slow wave propagation in the guinea-pig gastric antrum (Hirst et al. 2006, Edwards and Hirst 2006)", - "repository_type": "github", - "summary": "\"(Edwards and Hirst 2006) provides an electrical description of the propagation of slow waves and pacemaker potentials in the guinea-pig gastric antrum in anal and circumferential directions. As electrical conduction between laterally adjacent circular muscle bundles is regularly interrupted, anal conduction of pacemaker potentials was assumed to occur via an electrically interconnected chain of myenteric interstitial cells of Cajal (ICCMY). ICCMY were also connected resistively to serially connected compartments of longitudinal muscle. Circumferential conduction occurred in a circular smooth muscle bundle that was represented as a chain of electrically connected isopotential compartments: each compartment contained a proportion of intramuscular interstitial cells of Cajal (ICCIM) that are responsible for the regenerative component of the slow wave. The circular muscle layer, which contains ICCIM, and the ICCMY network incorporated a mechanism, modelled as a two-stage chemical reaction, which produces an intracellular messenger. ... The model generates pacemaker potentials and slow waves with propagation velocities similar to those determined in the physiological experiments described in the accompanying paper.\"", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 1165, - "tag": "ModelDB:87581" - } - ], - "timestamp_created": "2024-01-11 15:25:44.741925+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/87581", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "695": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 695, - "name": "Self-influencing synaptic plasticity (Tamosiunaite et al. 2007)", - "repository_type": "github", - "summary": "\"... Similar to a previous study (Saudargiene et al., 2004) we employ a differential\r\nHebbian learning rule to emulate spike-timing dependent\r\nplasticity and investigate how the interaction of dendritic\r\nand back-propagating spikes, as the post-synaptic signals,\r\ncould influence plasticity. ...\"", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 802, - "tag": "STDP" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 822, - "tag": "Winner-take-all" - }, - { - "id": 1166, - "tag": "ModelDB:87582" - } - ], - "timestamp_created": "2024-01-11 15:25:45.366095+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/87582", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "696": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 696, - "name": "Sodium channel mutations causing generalized epilepsy with febrile seizures + (Barela et al. 2006)", - "repository_type": "github", - "summary": "A novel mutation, R859C, in the Nav1.1 sodium channel was identified in a 4-generation, 33-member Caucasian family with a clinical presentation consistent with GEFS+. The mutation neutralizes a positively charged arginine in the domain 2 S4 voltage sensor of the Nav1.1 channel \u0192\u00d1 subunit. When the mutation was placed in the rat Nav1.1 channel and expressed in Xenopus oocytes, the mutant channel displayed a positive shift in the voltage-dependence of sodium channel activation, slower recovery from slow inactivation, and lower levels of current compared to the wild-type channel. Computational analysis suggests that neurons expressing the mutant channel have higher thresholds for firing a single action potential and for firing multiple action potentials, along with decreased repetitive firing. Therefore, this mutation should lead to decreased neuronal excitability, in contrast to most previous GEFS+ sodium channel mutations that have changes predicted to increase neuronal firing.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1167, - "tag": "ModelDB:87585" - } - ], - "timestamp_created": "2024-01-11 15:25:45.829199+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/87585", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "697": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 697, - "name": "Cat auditory nerve model (Zilany and Bruce 2006, 2007)", - "repository_type": "github", - "summary": "\"This paper presents a computational model to simulate normal and impaired auditory-nerve (AN)\r\nfiber responses in cats. The model responses match physiological data over a wider dynamic range\r\nthan previous auditory models. This is achieved by providing two modes of basilar membrane\r\nexcitation to the inner hair cell (IHC) rather than one. ... The model responses are consistent with a wide range of\r\nphysiological data from both normal and impaired ears for stimuli presented at levels spanning the\r\ndynamic range of hearing.\"", - "tags": [ - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 791, - "tag": "Rate-coding model neurons" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 1168, - "tag": "ModelDB:87751" - } - ], - "timestamp_created": "2024-01-11 15:25:46.313777+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/87751", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "698": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 698, - "name": "Cochlear implant models (Bruce et al. 1999a, b, c, 2000)", - "repository_type": "github", - "summary": "\"In a recent set of modeling studies we have developed a stochastic threshold model of auditory nerve response to single biphasic electrical pulses (Bruce et al., 1999c) and moderate rate (less than 800 pulses per second) pulse trains (Bruce et al., 1999a). In this article we derive an analytical approximation for the single-pulse model, which is then extended to describe the pulse-train model in the case of evenly timed, uniform pulses. This renewal-process description provides an accurate and computationally efficient model of electrical stimulation of single auditory nerve fibers by a cochlear implant that may be extended to other forms of electrical neural stimulation.\"", - "tags": [ - { - "id": 762, - "tag": "Audition" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 1169, - "tag": "ModelDB:87760" - } - ], - "timestamp_created": "2024-01-11 15:25:46.797504+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/87760", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "699": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 699, - "name": "The role of ATP-sensitive potassium channels in a hippocampal neuron (Huang et al. 2007)", - "repository_type": "github", - "summary": "\"Hyperglycemia-related neuronal excitability and epileptic seizures \r\nare not uncommon in clinical practice. However, their underlying \r\nmechanism remains elusive. ATP-sensitive K(+) (K(ATP)) channels are \r\nfound in many excitable cells, including cardiac myocytes, \r\npancreatic beta cells, and neurons. These channels provide a link \r\nbetween the electrical activity of cell membranes and cellular \r\nmetabolism. We investigated the effects of higher extracellular \r\nglucose on hippocampal K(ATP) channel activities and neuronal \r\nexcitability. The cell-attached patch-clamp configuration on \r\ncultured hippocampal cells and a novel multielectrode recording \r\nsystem on hippocampal slices were employed. In addition, a \r\nsimulation modeling hippocampal CA3 pyramidal neurons (Pinsky-Rinzel \r\nmodel) was analyzed to investigate the role of K(ATP) channels in \r\nthe firing of simulated action potentials. ...\"", - "tags": [ - { - "id": 795, - "tag": "ATP-senstive potassium current" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1170, - "tag": "ModelDB:87762" - } - ], - "timestamp_created": "2024-01-11 15:25:47.274602+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/87762", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "700": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 700, - "name": "Olfactory bulb network model of gamma oscillations (Bathellier et al. 2006; Lagier et al. 2007)", - "repository_type": "github", - "summary": "This model implements a network of 100 mitral cells connected with \r\nasynchronous inhibitory \"synapses\" that is meant to reproduce the \r\nGABAergic transmission of ensembles of connected granule cells. \r\nFor appropriate parameters of this special synapse the model generates \r\ngamma oscillations with properties very similar to what is observed \r\nin olfactory bulb slices (See Bathellier et al. 2006, Lagier et al. 2007).\r\nMitral cells are modeled as single compartment neurons with a small \r\nnumber of different voltage gated channels. Parameters were tuned to reproduce the\r\nfast subthreshold oscillation of the membrane potential observed experimentally\r\n(see Desmaisons et al. 1999).\r\n", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 754, - "tag": "Delay" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 1171, - "tag": "ModelDB:91387" - } - ], - "timestamp_created": "2024-01-11 15:25:47.815939+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/91387", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "701": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 701, - "name": "Oxytocin and VIP involvement in prolactin secretion (Egli et al. 2004,2006, Bertram et al. 2006)", - "repository_type": "github", - "summary": "\"Prolactin (PRL) is secreted from lactotrophs of the anterior\r\npituitary gland of rats in a unique pattern in response to\r\nuterine cervical stimulation (CS) during mating. Surges of\r\nPRL secretion occur in response to relief from hypothalamic\r\ndopaminergic inhibition and stimulation by hypothalamic releasing\r\nneurohormones. In this study, we characterized the\r\nrole of oxytocin (OT) in this system and the involvement of\r\nvasoactive intestinal polypeptide (VIP) from the suprachiasmatic\r\nnucleus (SCN) in controlling OT and PRL secretion of\r\nCS rats. ... OT\r\nmeasurements of serial blood samples obtained from ovariectomized\r\n(OVX) CS rats displayed a prominent increase at\r\nthe time of the afternoon PRL peak. The injection of VIP antisense\r\noligonucleotides into the SCN abolished the afternoon\r\nincrease of OT and PRL in CS-OVX animals. These findings\r\nsuggest that VIP from the SCN contributes to the regulation\r\nof OT and PRL secretion in CS rats. We propose that in CS rats\r\nthe regulatory mechanism(s) for PRL secretion comprise coordinated\r\naction of neuroendocrine dopaminergic and OT\r\ncells, both governed by the daily rhythm of VIP-ergic output\r\nfrom the SCN. This hypothesis is illustrated with a mathematical model.\"", - "tags": [ - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 830, - "tag": "XPP (web link to model)" - }, - { - "id": 1172, - "tag": "ModelDB:91893" - } - ], - "timestamp_created": "2024-01-11 15:25:48.349691+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/91893", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "702": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 702, - "name": "Low dose of dopamine may stimulate prolactin secretion by increasing K currents (Tabak et al. 2006)", - "repository_type": "github", - "summary": "\".. We considered\r\nthe fast K+ currents flowing through large-conductance\r\nBK channels and through A-type channels. We developed a\r\nminimal lactotroph model to investigate the effects of these\r\ntwo currents. Both IBK and IA could transform the electrical\r\npattern of activity from spiking to bursting, but through\r\ndistinct mechanisms. IBK always increased the intracellular\r\nCa2+ concentration, while IA could either increase or\r\ndecrease it. Thus, the stimulatory effects of DA could be\r\nmediated by a fast K+ conductance which converts tonically\r\nspiking cells to bursters. In addition, the study illustrates that\r\na heterogeneous distribution of fast K+ conductances could\r\ncause heterogeneous lactotroph firing patterns.\"", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 830, - "tag": "XPP (web link to model)" - }, - { - "id": 1173, - "tag": "ModelDB:91898" - } - ], - "timestamp_created": "2024-01-11 15:25:48.950482+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/91898", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "703": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 703, - "name": "Endothelin action on pituitary latotrophs (Bertram et al. 2006)", - "repository_type": "github", - "summary": "Endothelin (ET-1, -2, and -3 designate three genes which produce different endothelin isopeptides) is a prolactin inhibiting substance of hypothalmic origin. ET-1 binding is part of at least four G protein signaling pathways in lactotrophs. The sequence of events in these pathways following the presentation of nano- and pico-molar concentrations of ET-1 is modeled in the paper.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 818, - "tag": "I_SERCA" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 751, - "tag": "Signaling pathways" - }, - { - "id": 830, - "tag": "XPP (web link to model)" - }, - { - "id": 1174, - "tag": "ModelDB:91899" - } - ], - "timestamp_created": "2024-01-11 15:25:49.682431+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/91899", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "704": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 704, - "name": "Implementation issues in approximate methods for stochastic Hodgkin-Huxley models (Bruce 2007)", - "repository_type": "github", - "summary": "Four different algorithms for implementing Hodgkin\u2013Huxley models\r\nwith stochastic sodium channels: Strassberg and\r\nDeFelice (1993), Rubinstein (1995), Chow and White\r\n(1996), and Fox (1997) are compared.", - "tags": [ - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1175, - "tag": "ModelDB:93315" - } - ], - "timestamp_created": "2024-01-11 15:25:50.140702+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/93315", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "705": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 705, - "name": "Lamprey spinal CPG neuron (Huss et al. 2007)", - "repository_type": "github", - "summary": "This is a model of a generic locomotor network neuron in the lamprey spinal cord. The given version is assumed to correspond to an interneuron; motoneurons can also be modelled by changing the dendritic tree morphology.", - "tags": [ - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 783, - "tag": "I_Ks" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 573, - "tag": "Rebound firing" - }, - { - "id": 1176, - "tag": "ModelDB:93319" - } - ], - "timestamp_created": "2024-01-11 15:25:50.619324+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/93319", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "706": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 706, - "name": "Activity dependent conductances in a neuron model (Liu et al. 1998)", - "repository_type": "github", - "summary": "\"... We present a model of a\r\nstomatogastric ganglion (STG) neuron in which several Ca2+-dependent\r\npathways are used to regulate the maximal conductances of membrane\r\ncurrents in an activity-dependent manner. Unlike previous models of\r\nthis type, the regulation and modification of maximal conductances by\r\nelectrical activity is unconstrained. The model has seven\r\nvoltage-dependent membrane currents and uses three Ca2+ sensors acting\r\non different time scales. ... The model suggests that neurons may regulate their\r\nconductances to maintain fixed patterns of electrical activity, rather\r\nthan fixed maximal conductances, and that the regulation process\r\nrequires feedback systems capable of reacting to changes of electrical\r\nactivity on a number of different time scales.\"", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 75, - "tag": "Homeostasis" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 1177, - "tag": "ModelDB:93321" - } - ], - "timestamp_created": "2024-01-11 15:25:51.109482+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/93321", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "707": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 707, - "name": "Updated Tritonia Swim CPG (Calin-Jagemann et al. 2007)", - "repository_type": "github", - "summary": "Model of the 3-cell core CPG (DSI, C2, and VSI-B) mediating escape swimming in Tritonia diomedea. Cells use a hybrid integrate-and-fire scheme pioneered by Peter Getting. Each model cell is reconstructed from extensive physiological measurements to precisely mimic I-F curves, synaptic waveforms, and functional connectivity.", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 1178, - "tag": "ModelDB:93325" - } - ], - "timestamp_created": "2024-01-11 15:25:51.590555+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/93325", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "708": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 708, - "name": "Classic model of the Tritonia Swim CPG (Getting, 1989)", - "repository_type": "github", - "summary": "Classic model developed by Petter Getting of the 3-cell core CPG (DSI, C2, and VSI-B) mediating escape swimming in Tritonia diomedea. Cells use a hybrid integrate-and-fire scheme pioneered by Peter Getting. Each model cell is reconstructed from extensive physiological measurements to precisely mimic I-F curves, synaptic waveforms, and functional connectivity. **However, continued physiological measurements show that Getting may have inadvertently incorporated modulatory and or polysynaptic effects -- the properties of this model do *not* match physiological measurements in rested preparations.** This simulation reconstructs the Getting model as reported in: Getting (1989) 'Reconstruction of small neural networks' In Methods in Neural Modeling, 1st ed, p. 171-196. See also, an earlier version of this model reported in Getting (1983). Every attempt has been made to replicate the 1989 model as precisely as possible.", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 1179, - "tag": "ModelDB:93326" - } - ], - "timestamp_created": "2024-01-11 15:25:52.094964+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/93326", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "709": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 709, - "name": "Pyramidal Neuron Deep: Constrained by experiment (Dyhrfjeld-Johnsen et al. 2005)", - "repository_type": "github", - "summary": "\"... As a practical demonstration of the use of CoCoDat we\r\nconstructed a detailed computer model of an intrinsically\r\nbursting (IB) layer V pyramidal neuron from the rat barrel\r\ncortex supplementing experimental data (Schubert et al.,\r\n2001) with information extracted from the database. The\r\npyramidal neuron morphology (Fig. 10B) was reconstructed\r\nfrom histological sections of a biocytin-stained IB neuron\r\nusing the NeuroLucida software package...\"", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1180, - "tag": "ModelDB:93349" - } - ], - "timestamp_created": "2024-01-11 15:25:52.582274+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/93349", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "710": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 710, - "name": "Efficient estimation of detailed single-neuron models (Huys et al. 2006)", - "repository_type": "github", - "summary": "\"Biophysically accurate multicompartmental models of individual neurons ... depend on a large number of parameters that are difficult to estimate. ... We propose a statistical approach to the automatic estimation of various biologically relevant parameters, including 1) the distribution of channel densities, 2) the spatiotemporal pattern of synaptic input, and 3) axial resistances across extended dendrites. ... We demonstrate that the method leads to accurate estimations on a wide variety of challenging model data sets that include up to about 10,000 parameters (roughly two orders of magnitude more than previously feasible) and describe how the method gives insights into the functional interaction of groups of channels.\"", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1181, - "tag": "ModelDB:93390" - } - ], - "timestamp_created": "2024-01-11 15:25:53.057079+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/93390", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "711": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 711, - "name": "Fast population coding (Huys et al. 2007)", - "repository_type": "github", - "summary": "\"Uncertainty coming from the noise in its neurons and the ill-posed nature of many tasks plagues neural computations. Maybe surprisingly, many studies show that the brain manipulates these forms of uncertainty in a probabilistically consistent and normative manner, and there is now a rich theoretical literature on the capabilities of populations of neurons to implement computations in the face of uncertainty. However, one major facet of uncertainty has received comparatively little attention: time. In a dynamic, rapidly changing world, data are only temporarily relevant. Here, we analyze the computational consequences of encoding stimulus trajectories in populations of neurons. ...\"", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1182, - "tag": "ModelDB:93394" - } - ], - "timestamp_created": "2024-01-11 15:25:53.547720+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/93394", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "712": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 712, - "name": "Mapping function onto neuronal morphology (Stiefel and Sejnowski 2007)", - "repository_type": "github", - "summary": "\"... We used an optimization procedure to find neuronal morphological\r\nstructures for two computational tasks: First, neuronal morphologies were selected for\r\nlinearly summing excitatory synaptic potentials (EPSPs); second, structures were\r\nselected that distinguished the temporal order of EPSPs. The solutions resembled the\r\nmorphology of real neurons. In particular the neurons optimized for linear summation\r\nelectrotonically separated their synapses, as found in avian nucleus laminaris neurons,\r\nand neurons optimized for spike-order detection had primary dendrites of significantly\r\ndifferent diameter, as found in the basal and apical dendrites of cortical pyramidal\r\nneurons. ...\"", - "tags": [ - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 803, - "tag": "Unsupervised Learning" - }, - { - "id": 1183, - "tag": "ModelDB:93398" - } - ], - "timestamp_created": "2024-01-11 15:25:54.008011+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/93398", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "713": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 713, - "name": "Periodicity in Na channel properties alters model neuron excitability (Majumdar and Sikdar 2007)", - "repository_type": "github", - "summary": "\"... We have shown earlier that the\r\nduration and amplitude of a prolonged depolarization alter all the steady state and kinetic parameters of rNav1.2a voltage gated Na\r\nchannel in a pseudo-oscillatory fashion. In the present study, we show that the Hodgkin\u2013Huxley voltage and time dependent rate constants\r\nof activation (am and bm) and fast inactivation (ah and bh), obtained from the analyses of Na currents and steady state activation\r\nand inactivation plots, following application of prepulses in both slow (1\u2013100 s) and fast (100\u20131000 ms) ranges, vary with the duration of\r\na prepulse in a pseudo-oscillatory manner. ...\"", - "tags": [ - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1184, - "tag": "ModelDB:93416" - } - ], - "timestamp_created": "2024-01-11 15:25:54.820369+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/93416", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "714": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 714, - "name": "A neural model of Parkinson`s disease (Cutsuridis and Perantonis 2006, Cutsuridis 2006, 2007)", - "repository_type": "github", - "summary": "\"A neural model of neuromodulatory (dopamine) control of arm movements in Parkinson\u2019s disease (PD) bradykinesia was recently introduced [1, 2]. The model is multi-modular consisting of a basal ganglia module capable of selecting the most appropriate motor command in a given context, a cortical module for coordinating and executing the final motor commands, and a spino-musculo-skeletal module for guiding the arm to its final target and providing proprioceptive (feedback) input of the current state of the muscle and arm to higher cortical and lower spinal centers.\r\n... The new (extended) model [3] predicted that the reduced reciprocal disynaptic Ia inhibition in the DA depleted case doesn\u2019t lead to the co-contraction of antagonist motor units.\" See below readme and papers for more and details.", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 787, - "tag": "Pathophysiology" - }, - { - "id": 1185, - "tag": "ModelDB:93422" - } - ], - "timestamp_created": "2024-01-11 15:25:55.310252+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/93422", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "715": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 715, - "name": "Optimal deep brain stimulation of the subthalamic nucleus-a computational study (Feng et al. 2007)", - "repository_type": "github", - "summary": "Here, we use a biophysically-based model of spiking cells in the basal ganglia (Terman et al., Journal of Neuroscience, 22, 2963-2976, 2002; Rubin and Terman, Journal of Computational Neuroscience, 16, 211-235, 2004) to provide computational evidence that alternative temporal patterns of DBS inputs might be equally effective as the standard high-frequency waveforms, but require lower amplitudes. Within this model, DBS performance is assessed in two ways. First, we determine the extent to which DBS causes Gpi (globus pallidus pars interna) synaptic outputs, which are burstlike and synchronized in the unstimulated Parkinsonian state, to cease their pathological modulation of simulated thalamocortical cells. Second, we evaluate how DBS affects the GPi cells' auto- and cross-correlograms.", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 831, - "tag": "Deep brain stimulation" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 1186, - "tag": "ModelDB:93449" - } - ], - "timestamp_created": "2024-01-11 15:25:55.798154+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/93449", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "716": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 716, - "name": "Inferring connection proximity in electrically coupled networks (Cali et al. 2007)", - "repository_type": "github", - "summary": "In order to explore electrical coupling in the nervous system and its network-level organization, it is imperative to map the electrical synaptic microcircuits, in analogy with in vitro studies on monosynaptic and disynaptic chemical coupling. However, walking from cell to cell over large distances with a glass pipette is challenging, and microinjection of (fluorescent) dyes diffusing through gap-junctions remains so far the only method available to decipher such microcircuits even though technical limitations exist.\r\n\r\nBased on circuit theory, we derived analytical descriptions of the AC electrical coupling in networks of isopotential cells. We then proposed an operative electrophysiological protocol to distinguish between direct electrical connections and connections involving one or more intermediate cells. \r\n\r\nThis method allows inferring the number of intermediate cells, generalizing the conventional coupling coefficient, which provides limited information. \r\n\r\nWe provide here some analysis and simulation scripts that used to test our method through computer simulations, in vitro recordings, theoretical and numerical methods.\r\n\r\nKey words: Gap-Junctions; Electrical Coupling; Networks; ZAP current; Impedance.\r\n", - "tags": [ - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 788, - "tag": "PSpice" - }, - { - "id": 832, - "tag": "Sspice Symbolic SPICE" - }, - { - "id": 1187, - "tag": "ModelDB:94321" - } - ], - "timestamp_created": "2024-01-11 15:25:56.403897+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/94321", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "717": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 717, - "name": "Turtle visual cortex model (Nenadic et al. 2003, Wang et al. 2005, Wang et al. 2006)", - "repository_type": "github", - "summary": "This is a model of the visual cortex of freshwater turtles that is based upon the\r\nknown anatomy and physiology of individual neurons. The model was published in three\r\npapers (Nenadic et al., 2003; Wang et al., 2005; Wang et al., 2006), which should be\r\nconsulted for full details on its construction. The model has also been used in several\r\npapers (Robbins and Senseman, 2004; Du et al., 2005; Du et al., 2006). It is\r\nimplemented in GENESIS (Bower and Beeman, 1998).", - "tags": [ - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 726, - "tag": "Vision" - }, - { - "id": 1188, - "tag": "ModelDB:94845" - } - ], - "timestamp_created": "2024-01-11 15:25:56.892282+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/94845", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "718": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 718, - "name": "Rod photoreceptor (Barnes and Hille 1989, Publio et al. 2006, Kourennyi and Liu et al. 2004)", - "repository_type": "github", - "summary": "This a conductance-based model of a rod photoreceptor cell based on other modeling works\r\n(Barnes and Hille 1989 and Publio et al. 2006 and Kourennyi and Liu et al. 2004 ). In this\r\nmodel four types of ionic channels identified in the inner segment of the rod: nonselective cation channel (h), delayed rectifying potassium channel (Kv), noninactivating potassium channel (Kx) and calcium channel (Ca) was used. \r\nThe model accurately reproduces the rod response when stimulated with a simulated photocurrent signal. We can show the effect of nonselective cation channel. The absence of this channel cause increasing the peak amplitude and the time to \r\nreach the peak of voltage response and absence of transient mode in this response.", - "tags": [ - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 780, - "tag": "I Cl,Ca" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1189, - "tag": "ModelDB:95870" - } - ], - "timestamp_created": "2024-01-11 15:25:57.365572+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/95870", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "719": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 719, - "name": "Dentate gyrus granule cell: subthreshold signal processing (Schmidt-Hieber et al. 2007)", - "repository_type": "github", - "summary": "Detailed compartmental cable models of 8 hippocampal granule cells of adult mice were obtained from dual patch-clamp whole-cell recordings and subsequent 3D reconstructions. This code allows to reproduce figures 6-8 from the paper.", - "tags": [ - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 1190, - "tag": "ModelDB:95960" - } - ], - "timestamp_created": "2024-01-11 15:25:57.831095+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/95960", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "720": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 720, - "name": "Kinetics of the P2X7 receptor as expressed in Xenopus oocytes (Riedel et al. 2007a,b)", - "repository_type": "github", - "summary": "\"Human P2X7 receptors were expressed in Xenopus laevis oocytes and\r\nsingle channels were recorded using the patch-clamp technique in the\r\noutside-out configuration. ATP4- evoked two types of P2X7\r\nreceptor-mediated single channel currents characterized by short-lived\r\nand long-lived openings. ... The kinetics of the short channel\r\nopenings at negative membrane potentials fitted well to a linear\r\nC-C-C-O model with two ATP4- binding steps at equal binding sites\r\n....\" and \"Using the patch-clamp method, we studied the influence of\r\nexternal alkali and organic monovalent cations on the single-channel\r\nproperties of the adenosine triphosphate (ATP)-activated recombinant\r\nhuman P2X(7) receptor.\" See the references for more.", - "tags": [ - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 775, - "tag": "Nociception" - }, - { - "id": 833, - "tag": "QuB" - }, - { - "id": 1191, - "tag": "ModelDB:95990" - } - ], - "timestamp_created": "2024-01-11 15:25:58.303048+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/95990", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "721": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 721, - "name": "Translating network models to parallel hardware in NEURON (Hines and Carnevale 2008)", - "repository_type": "github", - "summary": "Shows how to move a working network model written in NEURON from a serial processor to a parallel machine in such a way that the final result will produce numerically identical results on either serial or parallel hardware.", - "tags": [ - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 1192, - "tag": "ModelDB:96444" - } - ], - "timestamp_created": "2024-01-11 15:25:58.774534+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/96444", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "722": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 722, - "name": "Olfactory bulb mitral and granule cell: dendrodendritic microcircuits (Migliore and Shepherd 2008)", - "repository_type": "github", - "summary": "This model shows how backpropagating action potentials in the long lateral dendrites of mitral cells, together with granule cell actions on mitral cells within narrow columns forming glomerular units, can provide a mechanism to activate strong local inhibition between arbitrarily distant mitral cells. The simulations predict a new role for the dendrodendritic synapses in the multicolumnar organization of the granule cells.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 719, - "tag": "Pattern Recognition" - }, - { - "id": 1193, - "tag": "ModelDB:97263" - } - ], - "timestamp_created": "2024-01-11 15:25:59.271834+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/97263", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "723": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 723, - "name": "Vesicular pool simulations of synaptic depression (Aristizabal and Glavinovic 2004)", - "repository_type": "github", - "summary": "\"Synaptic release was simulated using a Simulink sequential storage model with three vesicular pools. Modeling was modular and easily extendable to the systems with greater number of vesicular pools, parallel input, or time-varying parameters. ... Finally, the method was tested experimentally using the rat phrenic-diaphragm neuromuscular junction.\"\r\nSee paper for more and details.", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 824, - "tag": "Simulink" - }, - { - "id": 1194, - "tag": "ModelDB:97274" - } - ], - "timestamp_created": "2024-01-11 15:25:59.767706+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/97274", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "724": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 724, - "name": "Model of DARPP-32 phosphorylation in striatal medium spiny neurons (Lindskog et al. 2006)", - "repository_type": "github", - "summary": "The work describes a model of how transient calcium and dopamine inputs might affect phosphorylation of DARPP-32 in the medium spiny neurons in the striatum. The model is relevant for understanding both the \"three-factor rule\" for synaptic plasticity in corticostriatal synapses, and also for relating reinforcement learning theories to biology.", - "tags": [ - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 809, - "tag": "Reinforcement Learning" - }, - { - "id": 751, - "tag": "Signaling pathways" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1195, - "tag": "ModelDB:97743" - } - ], - "timestamp_created": "2024-01-11 15:26:00.238453+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/97743", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "725": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 725, - "name": "Fast-spiking cortical interneuron (Golomb et al. 2007)", - "repository_type": "github", - "summary": "Cortical fast-spiking (FS) interneurons display highly variable electrophysiological properties. We hypothesize that this variability emerges naturally if one assumes a continuous distribution of properties in a small set of active channels. We construct a minimal, single-compartment conductance-based model of FS cells that includes transient Na+, delayed-rectifier K+, and slowly inactivating d-type K+ conductances. The model may display delay to firing. Stuttering (elliptic bursting) and subthreshold oscillations may be observed for small Na+ window current.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 754, - "tag": "Delay" - }, - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 747, - "tag": "I_KD" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 834, - "tag": "Stuttering" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1196, - "tag": "ModelDB:97747" - } - ], - "timestamp_created": "2024-01-11 15:26:00.735586+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/97747", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "726": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 726, - "name": "Ion channel modeling with whole cell and a genetic algorithm (Gurkiewicz and Korngreen 2007)", - "repository_type": "github", - "summary": "\"... Here we show that a genetic search algorithm in combination with a gradient descent algorithm can be used to\r\nfit whole-cell voltage-clamp data to kinetic models with a high degree of accuracy. Previously, ion channel stimulation\r\ntraces were analyzed one at a time, the results of these analyses being combined to produce a picture of channel\r\nkinetics. Here the entire set of traces from all stimulation protocols are analysed simultaneously. The algorithm was\r\ninitially tested on simulated current traces produced by several Hodgkin-Huxley\u2013like and Markov chain models of\r\nvoltage-gated potassium and sodium channels. ... Finally, the algorithm was used for finding the kinetic parameters of several voltage-gated\r\nsodium and potassium channels models by matching its results to data recorded from layer 5 pyramidal neurons\r\nof the rat cortex in the nucleated outside-out patch configuration. The minimization scheme gives electrophysiologists\r\na tool for reproducing and simulating voltage-gated ion channel kinetics at the cellular level.\"", - "tags": [ - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 748, - "tag": "Markov-type model" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 743, - "tag": "NEURON (web link to model)" - }, - { - "id": 1197, - "tag": "ModelDB:97756" - } - ], - "timestamp_created": "2024-01-11 15:26:01.244124+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/97756", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "727": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 727, - "name": "Study of augmented Rubin and Terman 2004 deep brain stim. model in Parkinsons (Pascual et al. 2006)", - "repository_type": "github", - "summary": "\" ... The model by Rubin and Terman [31] represents one of the most comprehensive and biologically plausible models of DBS published recently. We examined the validity of the model, replicated its simulations and tested its robustness. While our simulations partially reproduced the results presented by Rubin and Terman [31], several issues were raised including the high complexity of the model in its non simplified form, the lack of robustness of the model with respect to small perturbations, the nonrealistic representation of the thalamus and the absence of time delays. Computational models are indeed necessary, but they may not be sufficient in their current forms to explain the effect of chronic electrical stimulation on the activity of the basal ganglia (BG) network in PD.\"", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 831, - "tag": "Deep brain stimulation" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1198, - "tag": "ModelDB:97850" - } - ], - "timestamp_created": "2024-01-11 15:26:01.767981+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/97850", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "728": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 728, - "name": "Rejuvenation model of dopamine neuron (Chan et al. 2007)", - "repository_type": "github", - "summary": "Model files for the paper C. Savio Chan, et al. 'Rejuvenation' protects neurons in mouse models of Parkinson's disease, Nature 447, 1081-1086(28 June 2007).", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 1199, - "tag": "ModelDB:97860" - } - ], - "timestamp_created": "2024-01-11 15:26:02.292001+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/97860", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "729": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 729, - "name": "Mammalian Ventricular Cell (Beeler and Reuter 1977)", - "repository_type": "github", - "summary": "This classic model of ventricular myocardial fibres was implemented by Francois Gannier. \"... Four individual components of ionic current were formulated mathematically\r\nin terms of Hodgkin-Huxley type equations. The model incorporates\r\ntwo voltage- and time-dependent inward currents, the excitatory\r\ninward sodium current, illa, and a secondary or slow inward current,\r\nis, primarily carried by calcium ions. A time-independent outward\r\npotassium current, iK1, exhibiting inward-going rectification, and a voltage-\r\nand time-dependent outward current, i.1, primarily carried by potassium\r\nions, are further elements of the model....\"\r\n", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1200, - "tag": "ModelDB:97863" - } - ], - "timestamp_created": "2024-01-11 15:26:02.806788+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/97863", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "730": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 730, - "name": "NEURON interfaces to MySQL and the SPUD feature extraction algorithm (Neymotin et al. 2008)", - "repository_type": "github", - "summary": "See the readme.txt for information on setting up this interface to a MySQL server from the NEURON simulator. Note the SPUD feature extraction algorithm includes its own readme in the spud directory.", - "tags": [ - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1201, - "tag": "ModelDB:97868" - } - ], - "timestamp_created": "2024-01-11 15:26:03.378577+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/97868", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "731": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 731, - "name": "Neural Query System NQS Data-Mining From Within the NEURON Simulator (Lytton 2006)", - "repository_type": "github", - "summary": "NQS is a databasing program with a query command modeled loosely on the SQL select command.\r\nPlease see the manual NQS.pdf for details of use.\r\nAn NQS database must be populated with data to be used. This package includes MFP (model fingerprint) which provides an example of NQS use with the model provided in the modeldb folder (see readme for usage).", - "tags": [ - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1202, - "tag": "ModelDB:97874" - } - ], - "timestamp_created": "2024-01-11 15:26:03.843940+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/97874", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "732": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 732, - "name": "Determinants of fast calcium dynamics in dendritic spines and dendrites (Cornelisse et al. 2007)", - "repository_type": "github", - "summary": "\"... Calcium influx time course and calcium extrusion rate were both in the same range for spines and dendrites when fitted with a dynamic multi-compartment model that included calcium binding kinetics and diffusion. In a subsequent analysis we used this model to investigate which parameters are critical determinants in spine calcium dynamics. The model confirmed the experimental findings: a higher SVR (surface-to-volume ratio) is not sufficient by itself to explain the faster rise time kinetics in spines, but only when paired with a lower buffer capacity in spines. Simulations at zero calcium-dye conditions show that calmodulin is more efficiently activated in spines, which indicates that spine morphology and buffering conditions in neocortical spines favor synaptic plasticity. ...\"", - "tags": [ - { - "id": 835, - "tag": "CalC Calcium Calculator" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1203, - "tag": "ModelDB:97903" - } - ], - "timestamp_created": "2024-01-11 15:26:04.343772+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/97903", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "733": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 733, - "name": "Cell splitting in neural networks extends strong scaling (Hines et al. 2008)", - "repository_type": "github", - "summary": "Neuron tree topology equations can be split into two subtrees and solved\r\non different processors with no change in accuracy, stability, or\r\ncomputational effort; communication costs involve only sending and\r\nreceiving two double precision values by each subtree at each time step.\r\nApplication of the cell splitting method to two published\r\nnetwork models exhibits good runtime scaling on twice as many\r\nprocessors as could be effectively used with whole-cell balancing.\r\n", - "tags": [ - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1204, - "tag": "ModelDB:97917" - } - ], - "timestamp_created": "2024-01-11 15:26:04.801431+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/97917", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "734": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 734, - "name": "Roles of subthalamic nucleus and DBS in reinforcement conflict-based decision making (Frank 2006)", - "repository_type": "github", - "summary": "Deep brain stimulation (DBS) of the subthalamic nucleus dramatically improves the motor symptoms of Parkinson's disease, but causes cognitive side effects such as impulsivity. This model from Frank (2006) simulates the role of the subthalamic nucleus (STN) within the basal ganglia circuitry in decision making. The STN dynamically modulates network decision thresholds in proportion to decision conflict. The STN ``hold your horses'' signal adaptively allows the system more time to settle on the best choice when multiple options are valid. The model also replicates effects in Parkinson's patients on and off DBS in experiments designed to test the model (Frank et al, 2007).", - "tags": [ - { - "id": 771, - "tag": "Action Selection/Decision Making" - }, - { - "id": 831, - "tag": "Deep brain stimulation" - }, - { - "id": 807, - "tag": "Emergent/PDPplusplus" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 791, - "tag": "Rate-coding model neurons" - }, - { - "id": 573, - "tag": "Rebound firing" - }, - { - "id": 809, - "tag": "Reinforcement Learning" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 1205, - "tag": "ModelDB:97972" - } - ], - "timestamp_created": "2024-01-11 15:26:05.289637+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/97972", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "735": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 735, - "name": "Neuronal population models of intracerebral EEG (Wendling et al. 2005)", - "repository_type": "github", - "summary": "\"... In this study, the authors relate electrophysiologic patterns typically observed during the transition from interictal to ictal activity in human mesial temporal lobe epilepsy (MTLE) to mechanisms (at a neuronal population level) involved in seizure generation through a computational model of EEG activity. Intracerebral EEG signals recorded from hippocampus in five patients with MTLE during four periods (during interictal activity, just before seizure onset, during seizure onset, and during ictal activity) were used to identify the three main parameters of a model of hippocampus EEG activity (related to excitation, slow dendritic inhibition and fast somatic inhibition). ... . Results demonstrated that the model generates very realistic signals for automatically identified parameters. They also showed that the transition from interictal to ictal activity cannot be simply explained by an increase in excitation and a decrease in inhibition but rather by time-varying ensemble interactions between pyramidal cells and local interneurons projecting to either their dendritic or perisomatic region (with slow and fast GABAA kinetics). Particularly, during preonset activity, an increasing dendritic GABAergic inhibition compensates a gradually increasing excitation up to a brutal drop at seizure onset when faster oscillations (beta and low gamma band, 15 to 40 Hz) are observed. ... These findings obtained from model identification in human temporal lobe epilepsy are in agreement with some results obtained experimentally, either on animal models of epilepsy or on the human epileptic tissue.\"", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 836, - "tag": "GNUstep NeXTStep/OpenStep" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 791, - "tag": "Rate-coding model neurons" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 1206, - "tag": "ModelDB:97983" - } - ], - "timestamp_created": "2024-01-11 15:26:06.022723+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/97983", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "736": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 736, - "name": "Fully Implicit Parallel Simulation of Single Neurons (Hines et al. 2008)", - "repository_type": "github", - "summary": "A 3-d reconstructed\r\nneuron model can be simulated in parallel on a dozen or so processors and experience almost linear\r\nspeedup. Network models can be simulated when\r\nthere are more processors than cells.\r\n", - "tags": [ - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1207, - "tag": "ModelDB:97985" - } - ], - "timestamp_created": "2024-01-11 15:27:36.848723+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/97985", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "737": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 737, - "name": "Theta phase precession in a model CA3 place cell (Baker and Olds 2007)", - "repository_type": "github", - "summary": "\"... The present study concerns a neurobiologically based computational model of the emergence of theta phase precession in which the responses of a single model CA3 pyramidal cell are examined in the context of stimulation by realistic afferent spike trains including those of place cells in entorhinal cortex, dentate gyrus, and other CA3 pyramidal cells. \r\nSpike-timing dependent plasticity in the model CA3 pyramidal cell leads to a spatially correlated associational synaptic drive that subsequently creates a spatially asymmetric expansion of the model cell\u2019s place field. ...\r\nThrough selective manipulations of the model it is possible to decompose theta phase precession in CA3 into the separate contributing factors of inheritance from upstream afferents in the dentate gyrus and entorhinal cortex, the interaction of synaptically controlled increasing afferent drive with phasic inhibition, and the theta phase difference between dentate gyrus granule cell and CA3 pyramidal cell activity.\"", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 805, - "tag": "I Mixed" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 825, - "tag": "Learning" - }, - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 799, - "tag": "Place cell/field" - }, - { - "id": 809, - "tag": "Reinforcement Learning" - }, - { - "id": 802, - "tag": "STDP" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 1208, - "tag": "ModelDB:98003" - } - ], - "timestamp_created": "2024-01-11 15:27:37.361218+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/98003", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "738": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 738, - "name": "D2 dopamine receptor modulation of interneuronal activity (Maurice et al. 2004)", - "repository_type": "github", - "summary": "\"... Using a combination of electrophysiological, molecular, and computational approaches, the studies reported here show that D2 dopamine receptor modulation of Na+ currents underlying autonomous spiking contributes to a slowing of discharge rate, such as that seen in vivo. Four lines of evidence support this conclusion. ... Fourth, simulation of cholinergic interneuron pacemaking revealed that a modest increase in the entry of Na+ channels into the slow-inactivated state was sufficient to account for the slowing of pacemaker discharge. These studies establish a cellular mechanism linking dopamine and the reduction in striatal cholinergic interneuron activity seen in the initial stages of associative learning.\" See paper for more and details.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 1209, - "tag": "ModelDB:98005" - } - ], - "timestamp_created": "2024-01-11 15:27:37.921829+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/98005", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "739": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 739, - "name": "Fronto-parietal visuospatial WM model with HH cells (Edin et al 2007)", - "repository_type": "github", - "summary": "1) J Cogn Neurosci: 3 structural mechanisms that had been hypothesized to underlie vsWM development during childhood were evaluated by simulating the model and comparing results to fMRI. It was concluded that inter-regional synaptic connection strength cause vsWM development.\r\n\r\n2) J Integr Neurosci: Given the importance of fronto-parietal connections, we tested whether connection asymmetry affected resistance to distraction. We drew the conclusion that stronger frontal connections are beneficial. By comparing model results to EEG, we concluded that the brain indeed has stronger frontal-to-parietal connections than vice versa.", - "tags": [ - { - "id": 718, - "tag": "Attractor Neural Network" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 794, - "tag": "Working memory" - }, - { - "id": 1210, - "tag": "ModelDB:98017" - } - ], - "timestamp_created": "2024-01-11 15:27:38.443640+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/98017", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "740": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 740, - "name": "Studies of stimulus parameters for seizure disruption using NN simulations (Anderson et al. 2007)", - "repository_type": "github", - "summary": "Architecturally realistic neocortical model using seven classes of excitatory and inhibitory single compartment Hodgkin-Huxley cells. Wiring is adapted to minicolumn hypothesis and incorporates visual and neocortical data. Simulation demonstrates spontaneous bursting onset and cessation, and activity can be altered with external electric field.", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 726, - "tag": "Vision" - }, - { - "id": 1211, - "tag": "ModelDB:98902" - } - ], - "timestamp_created": "2024-01-11 15:27:39.327837+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/98902", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "741": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 741, - "name": "ERG current in repolarizing plateau potentials in dopamine neurons (Canavier et al 2007)", - "repository_type": "github", - "summary": "\"Blocking the small-conductance (SK) calcium-activated potassium channel promotes\r\nburst firing in dopamine neurons both in vivo and in vitro.\r\n...\r\nWe focus on the underlying plateau potential\r\noscillation generated in the presence of both apamin and TTX, so that\r\naction potentials are not considered. \r\n\r\nWe find that although the plateau\r\npotentials are mediated by a voltage-gated Ca2+ current, they do not\r\ndepend on the accumulation of cytosolic Ca2+, then use a computational\r\nmodel to test the hypothesis that the slowly voltage-activated\r\nether-a-go-go\u2013related gene (ERG) potassium current repolarizes the\r\nplateaus.\r\n\r\nThe model, which includes a material balance on calcium,\r\nis able to reproduce the time course of both membrane potential\r\nand somatic calcium concentration, and can also mimic the induction\r\nof plateau potentials by the calcium chelator BAPTA.\" See paper for more.", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 710, - "tag": "FORTRAN" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 789, - "tag": "I_HERG" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 1212, - "tag": "ModelDB:100603" - } - ], - "timestamp_created": "2024-01-11 15:27:39.835320+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/100603", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "742": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 742, - "name": "CA3 pyramidal neuron: firing properties (Hemond et al. 2008)", - "repository_type": "github", - "summary": "In the paper, this model was used to identify how relative differences in K+ conductances,\r\nspecifically KC, KM, & KD, between cells contribute to the different characteristics of the\r\nthree types of firing patterns observed experimentally.\r\n", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 731, - "tag": "I CAN" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1213, - "tag": "ModelDB:101629" - } - ], - "timestamp_created": "2024-01-11 15:27:40.355684+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/101629", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "743": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 743, - "name": "Leech S Cell: Modulation of Excitability by Serotonin (Burrell and Crisp 2008)", - "repository_type": "github", - "summary": "Serotonergic modulation of the afterhyperpolarization (AHP) contributes to the regulation of the excitability of the leech S cell, a neuron critical for sensitization of the shortening reflex. Pharmacological and physiological data suggest that three currents contribute to the S cell's afterhyperpolarization: a charybdotoxin-sensitive, fast calcium-dependent potassium current (fAHP); a tubocurare-sensitive, calcium-dependent potassium current (mAHP); and, a saxitoxin-sensitive, afterdepolarization current (ADP). This single-compartment model of the S cell is constructed using fAHP, mAHP and ADP currents, and shows that reduction of the conductances to mimic the effects of serotonin is sufficient to enhance excitability (repetitive firing).", - "tags": [ - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 757, - "tag": "SNNAP" - }, - { - "id": 1214, - "tag": "ModelDB:102279" - } - ], - "timestamp_created": "2024-01-11 15:27:40.872502+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/102279", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "744": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 744, - "name": "O-LM interneuron model (Lawrence et al. 2006)", - "repository_type": "github", - "summary": "Exploring the kinetics and distribution of the muscarinic potassium channel, IM, in 2 O-LM interneuron morphologies. Modulation of the ion channel by drugs such as XE991 (antagonist) and retigabine (agonist) are simulated in the models to examine the role of IM in spiking properties.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 1215, - "tag": "ModelDB:102288" - } - ], - "timestamp_created": "2024-01-11 15:27:41.364985+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/102288", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "745": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 745, - "name": "Elementary mechanisms producing facilitation of Cav2.1 (P/Q-type) channels", - "repository_type": "github", - "summary": "\"The regulation of Ca(V)2.1 (P/Q-type) channels by calmodulin (CaM) showcases the powerful Ca(2+) decoding capabilities of CaM in complex with the family of Ca(V)1-2 Ca(2+) channels. \r\n\r\nThroughout this family, CaM does not simply exert a binary on/off regulatory effect; rather, Ca(2+) binding to either the C- or N-terminal lobe of CaM alone can selectively trigger a distinct form of channel modulation. \r\n...\r\nCa(2+) binding to the C-terminal lobe induces Ca(2+)-dependent facilitation of opening (CDF), whereas the N-terminal lobe yields Ca(2+)-dependent inactivation of opening (CDI). \r\n...\r\nFurthermore, direct single-channel determinations of channel open probability (P(o)) and kinetic simulations demonstrate that CDF represents a genuine enhancement of open probability, without appreciable change of activation kinetics. \r\nThis enhanced-opening mechanism suggests that the CDF evoked during action-potential trains would produce not only larger, but longer-lasting Ca(2+) responses, an outcome with potential ramifications for short-term synaptic plasticity.\"\r\n", - "tags": [ - { - "id": 723, - "tag": "Facilitation" - }, - { - "id": 742, - "tag": "I p,q" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1216, - "tag": "ModelDB:102871" - } - ], - "timestamp_created": "2024-01-11 15:27:41.883254+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/102871", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "746": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 746, - "name": "Ionic basis of alternans and Timothy Syndrome (Fox et al. 2002), (Zhu and Clancy 2007)", - "repository_type": "github", - "summary": "From Zhu and Clancy: \"... Here we\r\nemploy theoretical simulations to examine the effects of a Timothy Syndrome (TS) mutation\r\nin the L-type Ca2+ channel on cardiac dynamics over multiple\r\nscales, from a gene mutation to protein, cell, tissue, and finally the\r\nECG, to connect a defective Ca2+ channel to arrhythmia susceptibility. ...\"", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 737, - "tag": "Heart disease" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 783, - "tag": "I_Ks" - }, - { - "id": 785, - "tag": "Long-QT" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 738, - "tag": "Na/Ca exchanger" - }, - { - "id": 740, - "tag": "Na/K pump" - }, - { - "id": 741, - "tag": "Sodium pump" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 837, - "tag": "Timothy Syndrome" - }, - { - "id": 1217, - "tag": "ModelDB:104623" - } - ], - "timestamp_created": "2024-01-11 15:27:42.374105+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/104623", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "747": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 747, - "name": "Excitation-contraction coupling/mitochondrial energetics (ECME) model (Cortassa et al. 2006)", - "repository_type": "github", - "summary": "\"An intricate network of reactions is involved in matching energy supply with demand in the heart. This complexity\r\narises because energy production both modulates and is modulated by the electrophysiological and contractile activity of the\r\ncardiac myocyte. Here, we present an integrated mathematical model of the cardiac cell that links excitation-contraction\r\ncoupling with mitochondrial energy generation. The dynamics of the model are described by a system of 50 ordinary differential\r\nequations. The formulation explicitly incorporates cytoplasmic ATP-consuming processes associated with force generation and\r\nion transport, as well as the creatine kinase reaction. Changes in the electrical and contractile activity of the myocyte are\r\ncoupled to mitochondrial energetics through the ATP, Ca21, and Na1 concentrations in the myoplasmic and mitochondrial\r\nmatrix compartments. ...\"", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 818, - "tag": "I_SERCA" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 738, - "tag": "Na/Ca exchanger" - }, - { - "id": 751, - "tag": "Signaling pathways" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 1218, - "tag": "ModelDB:105383" - } - ], - "timestamp_created": "2024-01-11 15:27:42.903289+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/105383", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "748": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 748, - "name": "Nav1.6 sodium channel model in globus pallidus neurons (Mercer et al. 2007)", - "repository_type": "github", - "summary": "Model files for the paper Mercer JN, Chan CS, Tkatch T, Held J, Surmeier DJ. Nav1.6 sodium channels are critical to pacemaking and fast spiking in globus pallidus neurons.,J Neurosci. 2007 Dec 5;27(49):13552-66.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 1219, - "tag": "ModelDB:105385" - } - ], - "timestamp_created": "2024-01-11 15:27:43.434619+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/105385", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "749": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 749, - "name": "Temporal integration by stochastic recurrent network (Okamoto et al. 2007)", - "repository_type": "github", - "summary": "\"Temporal integration of externally or internally\r\ndriven information is required for a variety of cognitive\r\nprocesses. This computation is generally linked with graded rate\r\nchanges in cortical neurons, which typically appear during a delay\r\nperiod of cognitive task in the prefrontal and other cortical areas.\r\nHere, we present a neural network model to produce graded (climbing\r\nor descending) neuronal activity. Model neurons are interconnected\r\nrandomly by AMPA-receptor\u2013mediated fast excitatory synapses and\r\nare subject to noisy background excitatory and inhibitory synaptic\r\ninputs. In each neuron, a prolonged afterdepolarizing potential follows\r\nevery spike generation. Then, driven by an external input, the individual\r\nneurons display bimodal rate changes between a baseline state\r\nand an elevated firing state, with the latter being sustained by regenerated\r\nafterdepolarizing potentials. ...\"", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1220, - "tag": "ModelDB:105501" - } - ], - "timestamp_created": "2024-01-11 15:27:44.073303+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/105501", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "750": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 750, - "name": "A dual-Ca2+-sensor model for neurotransmitter release in a central synapse (Sun et al. 2007)", - "repository_type": "github", - "summary": "\"Ca2+-triggered synchronous neurotransmitter release is well described, but asynchronous release-in fact, its very existence-remains enigmatic. \r\nHere we report a quantitative description of asynchronous neurotransmitter release in calyx-of-Held synapses. \r\n...\r\nOur results reveal that release triggered in wild-type synapses at low Ca2+ concentrations is physiologically asynchronous, and that asynchronous release completely empties the readily releasable pool of vesicles during sustained elevations of Ca2+. \r\nWe propose a dual-Ca2+-sensor model of release that quantitatively describes the contributions of synchronous and asynchronous release under conditions of different presynaptic Ca2+ dynamics.\"", - "tags": [ - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 820, - "tag": "IGOR Pro" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1221, - "tag": "ModelDB:105506" - } - ], - "timestamp_created": "2024-01-11 15:27:44.739858+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/105506", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "751": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 751, - "name": "Tonic-clonic transitions in a seizure simulation (Lytton and Omurtag 2007)", - "repository_type": "github", - "summary": "\"... The authors have ... computationally manageable networks of moderate size consisting of 1,000 to 3,000 neurons with multiple intrinsic\r\nand synaptic properties. \r\n\r\nExperiments on these simulations demonstrated the presence of epileptiform behavior in the form of\r\nrepetitive high-intensity population events (clonic behavior) or\r\nlatch-up with near maximal activity (tonic behavior). \r\n\r\n...\r\nSeveral simulations revealed the importance of random coincident inputs to shift a network from\r\na low-activation to a high-activation epileptiform state. Finally, a\r\nsimulated anticonvulsant acting on excitability tended to preferentially\r\ndecrease tonic activity.\"\r\n", - "tags": [ - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1222, - "tag": "ModelDB:105507" - } - ], - "timestamp_created": "2024-01-11 15:27:45.363204+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/105507", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "752": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 752, - "name": "Effect of riluzole on action potential in cultured human skeletal muscle cells (Wang YJ et al. 2008)", - "repository_type": "github", - "summary": "Simulation studies also unraveled that both decreased conductance of I(Na) and increased conductance of I(K(Ca)) utilized to mimic riluzole\r\nactions in skeletal muscle cells could combine to decrease the amplitude of action potentials and increase the repolarization of action potentials.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1223, - "tag": "ModelDB:105528" - } - ], - "timestamp_created": "2024-01-11 15:27:45.912480+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/105528", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "753": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 753, - "name": "CA1 pyramidal neuron: calculation of MRI signals (Cassara et al. 2008)", - "repository_type": "github", - "summary": "NEURON mod files from the paper:\r\nCassar\u00e0 AM, Hagberg GE, Bianciardi M, Migliore M, Maraviglia B.\r\nRealistic simulations of neuronal activity: A contribution to the debate on direct detection of neuronal currents by MRI.\r\nNeuroimage. 39:87-106 (2008).\r\n\r\nIn this paper, we use a detailed calculation of the magnetic field produced by the neuronal \r\ncurrents propagating over a hippocampal CA1 pyramidal neuron placed inside a cubic MR voxel of \r\nlength 1.2 mm to estimate the Magnetic Resonance signal.\r\n", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 734, - "tag": "Extracellular Fields" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1224, - "tag": "ModelDB:106551" - } - ], - "timestamp_created": "2024-01-11 15:27:46.666266+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/106551", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "754": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 754, - "name": "JitCon: Just in time connectivity for large spiking networks (Lytton et al. 2008)", - "repository_type": "github", - "summary": "This simulation is primarily an illustration and is not well optimized for actually running large\r\n networks.\r\njitcon.mod contains a large amount of C level code, understanding of which requires some\r\n knowledge of Neuron internals", - "tags": [ - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1225, - "tag": "ModelDB:106891" - } - ], - "timestamp_created": "2024-01-11 15:27:47.156808+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/106891", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "755": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 755, - "name": "Calcium spikes in basal dendrites (Kampa and Stuart 2006)", - "repository_type": "github", - "summary": "This model was published in Kampa & Stuart (2006) J Neurosci 26(28):7424-32. The simulation creates two plots showing voltage and calcium changes in basal dendrites of layer 5 pyramidal neurons during action potential backpropagation. \r\n\r\ncreated by B. Kampa (2006)", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 1226, - "tag": "ModelDB:108458" - } - ], - "timestamp_created": "2024-01-11 15:27:47.648051+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/108458", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "756": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 756, - "name": "STDP depends on dendritic synapse location (Letzkus et al. 2006)", - "repository_type": "github", - "summary": "This model was published in Letzkus, Kampa & Stuart (2006) J Neurosci 26(41):10420-9. The simulation creates several plots showing voltage and NMDA current and conductance changes at different apical dendritic locations in layer 5 pyramidal neurons during STDP induction protocols. \r\n\r\nCreated by B. Kampa (2006).", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 802, - "tag": "STDP" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 1227, - "tag": "ModelDB:108459" - } - ], - "timestamp_created": "2024-01-11 15:27:48.198892+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/108459", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "757": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 757, - "name": "A microcircuit model of the frontal eye fields (Heinzle et al. 2007)", - "repository_type": "github", - "summary": "\" ... we show that the canonical circuit (Douglas et al. 1989, Douglas and Martin 1991) can,\r\nwith a few modifications, model the primate FEF. The spike-based network of integrate-and-fire neurons was tested in tasks that were\r\nused in electrophysiological experiments in behaving macaque monkeys. The dynamics of the model matched those of neurons observed\r\nin the FEF, and the behavioral results matched those observed in psychophysical experiments. The close relationship between the model\r\nand the cortical architecture allows a detailed comparison of the simulation results with physiological data and predicts details of the\r\nanatomical circuit of the FEF.\"", - "tags": [ - { - "id": 771, - "tag": "Action Selection/Decision Making" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 726, - "tag": "Vision" - }, - { - "id": 1228, - "tag": "ModelDB:110022" - } - ], - "timestamp_created": "2024-01-11 15:27:48.744047+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/110022", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "758": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 758, - "name": "Space clamp problems in neurons with voltage-gated conductances (Bar-Yehuda and Korngreen 2008)", - "repository_type": "github", - "summary": "\" ... using numerical simulations, we show that the distortions of voltage-gated K+ and Ca2+ currents are substantial even in neurons with short dendrites. The simulations also demonstrate that passive cable theory cannot be used to justify voltage-clamping of neurons, due to significant shunting to the reversal potential of the voltage-gated conductance during channel activation.\r\n... \"", - "tags": [ - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1229, - "tag": "ModelDB:110560" - } - ], - "timestamp_created": "2024-01-11 15:27:49.264577+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/110560", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "759": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 759, - "name": "Thalamic transformation of pallidal input (Hadipour-Niktarash 2006)", - "repository_type": "github", - "summary": "\"In Parkinson\u2019s disease, neurons of the internal segment of the globus pallidus (GPi) display the low-frequency tremor-related oscillations.\r\n\r\nThese oscillatory activities are transmitted to the thalamic relay nuclei.\r\n\r\nComputer models of the interacting thalamocortical (TC) and thalamic reticular (RE) neurons were used to explore how the TC-RE network processes the low-frequency oscillations of the GPi neurons. ...\"\r\n", - "tags": [ - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 1230, - "tag": "ModelDB:111870" - } - ], - "timestamp_created": "2024-01-11 15:27:49.774817+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/111870", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "760": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 760, - "name": "Ca-dependent K Channel: kinetics from rat muscle (Moczydlowski, Latorre 1983) XPP", - "repository_type": "github", - "summary": "This is an XPP version of the classic KCa channel from Moczydlowski and Latorre 1983.", - "tags": [ - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1231, - "tag": "ModelDB:111877" - } - ], - "timestamp_created": "2024-01-11 15:27:50.236614+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/111877", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "761": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 761, - "name": "Thalamocortical model of spike and wave seizures (Suffczynski et al. 2004)", - "repository_type": "github", - "summary": "SIMULINK macroscopic model of transitions between normal (spindle) activity and spike and wave (SW) discharges in the thalamocortical network. The model exhibits bistability properties and stochastic fluctuations present in the network may flip the system between the two operational states. The predictions of the model were compared with real EEG data in rats and humans. A possibility to abort an ictal state by a single counter stimulus is suggested by the model. \r\n", - "tags": [ - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 824, - "tag": "Simulink" - }, - { - "id": 593, - "tag": "Spindles" - }, - { - "id": 1232, - "tag": "ModelDB:111880" - } - ], - "timestamp_created": "2024-01-11 15:27:50.793670+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/111880", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "762": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 762, - "name": "Kinetic properties of voltage gated Na channel (Nayak and Sikdar 2007)", - "repository_type": "github", - "summary": "Here we illustrate novel non-linear properties of voltage gated Na+ channel induced by sustained membrane depolarization. In cell-attached patch clamp recordings of rNav1.2 channels expressed in CHO cells, we found complex non-linear changes in the molecular kinetic properties, including channel dwell times and unitary conductance of single Na+ channels that were dependent on the extent of conditioning membrane depolarization.\r\nA \u201cmolecular memory\u201d phenomenon arises at longer depolarization characterized by clusters of dwell time events and strong autocorrelation in dwell times. Hidden Markov Modeling (HMM) ... suggested a possible explanation to the memory phenomenon.\r\nSee paper for more and details.", - "tags": [ - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 748, - "tag": "Markov-type model" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 838, - "tag": "Yale HMM" - }, - { - "id": 1233, - "tag": "ModelDB:111967" - } - ], - "timestamp_created": "2024-01-11 15:27:51.265419+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/111967", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "763": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 763, - "name": "Properties of aconitine-induced block of KDR current in NG108-15 neurons (Lin et al. 2008)", - "repository_type": "github", - "summary": "\"The effects of aconitine (ACO), a highly toxic alkaloid, on ion currents in differentiated NG108-15 neuronal cells were investigated in this study. ACO (0.3-30 microM) suppressed the amplitude of delayed rectifier K+ current (IK(DR)) in a concentration-dependent manner with an IC50 value of 3.1 microM. The presence of ACO enhanced the rate and extent of IK(DR) inactivation, although it had no effect on the initial activation phase of IK(DR). ... A modeled cell was designed to duplicate its inhibitory effect on spontaneous pacemaking. ... Taken together, the experimental data and simulations show that ACO can block delayed rectifier K+ channels of neurons in a concentration- and state-dependent manner. Changes in action potentials induced by ACO in neurons in vivo can be explained mainly by its blocking actions on IK(DR) and INa.\"", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 769, - "tag": "I_KHT" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1234, - "tag": "ModelDB:112079" - } - ], - "timestamp_created": "2024-01-11 15:27:51.817919+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/112079", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "764": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 764, - "name": "Carbon nanotubes as electrical interfaces to neurons (Giugliano et al. 2008)", - "repository_type": "github", - "summary": "In the present NEURON model, we explore simple phenomenological models of the extracellular coupling, occurring at the neuron-metal microelectrode junction and (possibly) at the neuron-carbon nanotube junction.", - "tags": [ - { - "id": 734, - "tag": "Extracellular Fields" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1235, - "tag": "ModelDB:112086" - } - ], - "timestamp_created": "2024-01-11 15:27:52.292483+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/112086", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "765": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 765, - "name": "Activity constraints on stable neuronal or network parameters (Olypher and Calabrese 2007)", - "repository_type": "github", - "summary": "\"In this study, we developed a general description of parameter combinations for which specified\r\ncharacteristics of neuronal or network activity are constant.\r\nOur approach is based on the implicit function theorem and is applicable\r\nto activity characteristics that smoothly depend on parameters.\r\nSuch smoothness is often intrinsic to neuronal systems when they are in\r\nstable functional states.\r\nThe conclusions about how parameters compensate each other, developed in this study, can thus be used even\r\nwithout regard to the specific mathematical model describing a particular\r\nneuron or neuronal network. ...\"", - "tags": [ - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1236, - "tag": "ModelDB:112348" - } - ], - "timestamp_created": "2024-01-11 15:27:52.765620+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/112348", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "766": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 766, - "name": "Nigral dopaminergic neurons: effects of ethanol on Ih (Migliore et al. 2008)", - "repository_type": "github", - "summary": "We use a realistic computational model of dopaminergic neurons in vivo to suggest \r\nthat ethanol, through its effects on Ih, modifies the temporal structure of the spiking \r\nactivity. The model predicts that the dopamine level may increase much more during bursting \r\nthan pacemaking activity, especially in those brain regions with a slow dopamine clearance rate. \r\nThe results suggest that a selective pharmacological remedy could thus be devised against the \r\nrewarding effects of ethanol that are postulated to mediate alcohol abuse and addiction, \r\ntargeting the specific HCN genes expressed in dopaminergic neurons.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 839, - "tag": "Alcohol Use Disorder" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 740, - "tag": "Na/K pump" - }, - { - "id": 787, - "tag": "Pathophysiology" - }, - { - "id": 741, - "tag": "Sodium pump" - }, - { - "id": 1237, - "tag": "ModelDB:112359" - } - ], - "timestamp_created": "2024-01-11 15:27:53.376969+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/112359", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "767": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 767, - "name": "CellExcite: an efficient simulation environment for excitable cells (Bartocci et al. 2008)", - "repository_type": "github", - "summary": "\"We have developed CellExcite, a sophisticated simulation environment for excitable-cell networks. CellExcite allows the user to sketch a tissue of excitable cells, plan the stimuli to be applied during simulation, and customize the diffusion model. CellExcite adopts Hybrid Automata (HA) as the computational model in order to efficiently capture both discrete and continuous excitable-cell behavior.\"", - "tags": [ - { - "id": 840, - "tag": "CellExcite" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 1238, - "tag": "ModelDB:112468" - } - ], - "timestamp_created": "2024-01-11 15:27:53.853185+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/112468", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "768": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 768, - "name": "CA1 pyramidal neuron: functional significance of axonal Kv7 channels (Shah et al. 2008)", - "repository_type": "github", - "summary": "The model used in this paper confirmed the experimental findings suggesting that axonal Kv7 channels are critically and uniquely required for determining the inherent spontaneous firing of \r\nhippocampal CA1 pyramids, independently of alterations in synaptic activity. \r\nThe model predicts that the axonal Kv7 density could be 3-5 times that at the soma.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1239, - "tag": "ModelDB:112546" - } - ], - "timestamp_created": "2024-01-11 15:27:54.350438+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/112546", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "769": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 769, - "name": "Spontaneous calcium oscillations in astrocytes (Lavrentovich and Hemkin 2008)", - "repository_type": "github", - "summary": "\" ... We propose here a mathematical model of how spontaneous Ca2+ \r\noscillations arise in astrocytes. This model uses the calcium-induced calcium release and inositol cross-coupling \r\nmechanisms coupled with a receptor-independent method for producing inositol (1,4,5)-trisphosphate as the heart \r\nof the model. By computationally mimicking experimental constraints we have found that this model provides \r\nresults that are qualitatively similar to experiment.\"", - "tags": [ - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 790, - "tag": "Calcium waves" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 751, - "tag": "Signaling pathways" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1240, - "tag": "ModelDB:112547" - } - ], - "timestamp_created": "2024-01-11 15:27:54.834668+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/112547", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "770": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 770, - "name": "Gap-junction coupled network activity depends on coupled dendrites diameter (Gansert et al. 2007)", - "repository_type": "github", - "summary": "\"... We have previously shown that the amplitude of electrical signals propagating across gap-junctionally coupled passive cables is maximized at a unique diameter. This suggests that threshold-dependent signals may propagate through gap junctions for a finite range of diameters around this optimal value. \r\nHere we examine the diameter dependence of action potential propagation across model networks of dendro-dendritically coupled neurons. The neurons in these models have passive soma and dendrites and an action potential-generating axon. We show that propagation of action potentials across gap junctions occurs only over a finite range of dendritic diameters and that propagation delay depends on this diameter. ...\". See paper for more and details.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 604, - "tag": "Network" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 1241, - "tag": "ModelDB:112633" - } - ], - "timestamp_created": "2024-01-11 15:27:55.422998+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/112633", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "771": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 771, - "name": "Cerebellar Golgi cell (Solinas et al. 2007a, 2007b)", - "repository_type": "github", - "summary": "\"... Our results suggest that a complex complement of ionic mechanisms is needed to fine-tune separate aspects of the neuronal response dynamics. Simulations also suggest that the Golgi cell may exploit these mechanisms to obtain a fine regulation of timing of incoming mossy fiber responses and granular layer circuit oscillation and bursting.\"", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 773, - "tag": "neuroConstruct (web link to model)" - }, - { - "id": 1242, - "tag": "ModelDB:112685" - } - ], - "timestamp_created": "2024-01-11 15:27:55.904388+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/112685", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "772": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 772, - "name": "Afferent Integration in the NAcb MSP Cell (Wolf et al. 2005)", - "repository_type": "github", - "summary": "\"We describe a computational model of the principal cell in the nucleus accumbens (NAcb), the medium spiny projection (MSP) neuron. \r\n\r\nThe model neuron, constructed in NEURON, includes all of the known ionic currents in these cells and receives synaptic input from simulated spike trains via NMDA, AMPA, and GABAA receptors. \r\n\r\n... results suggest that afferent information integration by the NAcb MSP cell may be compromised by pathology in which the NMDA current is altered or modulated, as has been proposed in both schizophrenia and addiction.\"\r\n", - "tags": [ - { - "id": 841, - "tag": "Addiction" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 842, - "tag": "I Krp" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 843, - "tag": "I Q" - }, - { - "id": 844, - "tag": "I R" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 845, - "tag": "Schizophrenia" - }, - { - "id": 1243, - "tag": "ModelDB:112834" - } - ], - "timestamp_created": "2024-01-11 15:27:56.436248+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/112834", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "773": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 773, - "name": "Spike trains in Hodgkin\u2013Huxley model and ISIs of acupuncture manipulations (Wang et al. 2008)", - "repository_type": "github", - "summary": "The Hodgkin-Huxley equations (HH) are parameterized by a number of parameters \r\nand shows a variety of qualitatively different behaviors depending on the \r\nparameter values. Under stimulation of an external periodic voltage, the \r\nISIs (interspike intervals) of a HH model are investigated in this work, \r\nwhile the frequency of the voltage is taken as the controlling parameter. \r\nAs well-known, the science of acupuncture and moxibustion is an important \r\ncomponent of Traditional Chinese Medicine with a long history. Although there \r\nare a number of different acupuncture manipulations, the method for \r\ndistinguishing them is rarely investigated. With the idea of ISI, we study \r\nthe electrical signal time series at the spinal dorsal horn produced by \r\nthree different acupuncture manipulations in Zusanli point and present an \r\neffective way to distinguish them.\r\n", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 846, - "tag": "Complementary and alternative medicine" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1244, - "tag": "ModelDB:112836" - } - ], - "timestamp_created": "2024-01-11 15:27:56.930931+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/112836", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "774": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 774, - "name": "Two-cell inhibitory network bursting dynamics captured in a one-dimensional map (Matveev et al 2007)", - "repository_type": "github", - "summary": "\" ... Here\r\nwe describe a simple method that allows us to investigate\r\nthe existence and stability of anti-phase bursting\r\nsolutions in a network of two spiking neurons, each\r\npossessing a T-type calcium current and coupled by\r\nreciprocal inhibition. \r\nWe derive a one-dimensional map\r\nwhich fully characterizes the genesis and regulation of\r\nanti-phase bursting arising from the interaction of the\r\nT-current properties with the properties of synaptic\r\ninhibition. ...\"", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 1245, - "tag": "ModelDB:112914" - } - ], - "timestamp_created": "2024-01-11 15:27:57.406304+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/112914", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "775": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 775, - "name": "Spikes,synchrony,and attentive learning by laminar thalamocort. circuits (Grossberg & Versace 2007)", - "repository_type": "github", - "summary": "\"... The model\r\nhereby clarifies, for the first time, how the following levels of brain organization coexist to realize\r\ncognitive processing properties that regulate fast learning and stable memory of brain representations:\r\nsingle cell properties, such as spiking dynamics, spike-timing-dependent plasticity (STDP), and\r\nacetylcholine modulation; detailed laminar thalamic and cortical circuit designs and their interactions;\r\naggregate cell recordings, such as current-source densities and local field potentials; and single cell and\r\nlarge-scale inter-areal oscillations in the gamma and beta frequency domains. ...\"", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 847, - "tag": "KInNeSS (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 848, - "tag": "NeuroML (web link to model)" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 719, - "tag": "Pattern Recognition" - }, - { - "id": 802, - "tag": "STDP" - }, - { - "id": 800, - "tag": "Spatial Navigation" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 794, - "tag": "Working memory" - }, - { - "id": 829, - "tag": "XML (web link to model)" - }, - { - "id": 1246, - "tag": "ModelDB:112922" - } - ], - "timestamp_created": "2024-01-11 15:27:58.023623+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/112922", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "776": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 776, - "name": "Synchronization by D4 dopamine receptor-mediated phospholipid methylation (Kuznetsova, Deth 2008)", - "repository_type": "github", - "summary": "\"We describe a new molecular mechanism of\r\ndopamine-induced membrane protein modulation that\r\ncan tune neuronal oscillation frequency to attention related\r\ngamma rhythm. This mechanism is based on\r\nthe unique ability of D4 dopamine receptors (D4R)\r\nto carry out phospholipid methylation (PLM) that\r\nmay affect the kinetics of ion channels.\r\nWe show that by deceasing the inertia of the delayed rectifier potassium channel, a transition to 40 Hz oscillations can be achieved. ...\"", - "tags": [ - { - "id": 751, - "tag": "Signaling pathways" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 1247, - "tag": "ModelDB:112968" - } - ], - "timestamp_created": "2024-01-11 15:27:58.520452+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/112968", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "777": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 777, - "name": "Stability of complex spike timing-dependent plasticity in cerebellar learning (Roberts 2007)", - "repository_type": "github", - "summary": "\"Dynamics of spike-timing dependent synaptic\r\nplasticity are analyzed for excitatory and inhibitory synapses\r\nonto cerebellar Purkinje cells. \r\n\r\nThe purpose of this study is\r\nto place theoretical constraints on candidate synaptic learning\r\nrules that determine the changes in synaptic efficacy\r\ndue to pairing complex spikes with presynaptic spikes in\r\nparallel fibers and inhibitory interneurons. \r\n...\"", - "tags": [ - { - "id": 849, - "tag": "Java (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 802, - "tag": "STDP" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 1248, - "tag": "ModelDB:113426" - } - ], - "timestamp_created": "2024-01-11 15:27:59.033859+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/113426", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "778": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 778, - "name": "Computer simulations of neuron-glia interactions mediated by ion flux (Somjen et al. 2008)", - "repository_type": "github", - "summary": "\"...\r\nTo examine the effect of glial K+ uptake, we used a model neuron equipped with Na+, K+, Ca2+ and Cl−\r\nconductances, ion pumps and ion exchangers, surrounded by interstitial space and glia. \r\nThe glial membrane was either \u201cpassive\u201d, incorporating only leak channels and an ion\r\nexchange pump, or it had rectifying K+ channels. We computed ion fluxes, concentration changes and osmotic\r\nvolume changes.\r\n...\r\nWe conclude that voltage gated K+ currents can boost the effectiveness of the glial \u201cpotassium buffer\u201d\r\nand that this buffer function is important even at moderate or low levels of excitation, but especially so in pathological states.\"", - "tags": [ - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 738, - "tag": "Na/Ca exchanger" - }, - { - "id": 740, - "tag": "Na/K pump" - }, - { - "id": 741, - "tag": "Sodium pump" - }, - { - "id": 1249, - "tag": "ModelDB:113446" - } - ], - "timestamp_created": "2024-01-11 15:27:59.538252+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/113446", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "779": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 779, - "name": "Distributed representation of perceptual categories in the auditory cortex (Kim and Bao 2008)", - "repository_type": "github", - "summary": "Examines the hypothesis that enlargement in cortical stimulus representation is a mechanism of categorical perception. Categorical perception is tested using discrimination and identification ability.", - "tags": [ - { - "id": 762, - "tag": "Audition" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 850, - "tag": "Perceptual Categories" - }, - { - "id": 1250, - "tag": "ModelDB:113459" - } - ], - "timestamp_created": "2024-01-11 15:28:00.028285+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/113459", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "780": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 780, - "name": "Multiple mechanisms of short term plasticity at the calyx of Held (Hennig et al. 2008)", - "repository_type": "github", - "summary": "This is a new model of the short-term dynamics of glutamatergic synaptic transmission, which incorporates multiple mechanisms acting at differing sites and across a range of different time scales (ms to tens of seconds). In the paper, we show that this model can accurately reproduce the experimentally measured time-course of short term depression across different stimulus frequencies at the calyx of Held. The model demonstrates how multiple forms of activity-dependent modulation of release probability and vesicle pool depletion interact, and shows how stimulus-history-dependent recovery from synaptic depression can arise from dynamics on multiple time scales.", - "tags": [ - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - }, - { - "id": 1251, - "tag": "ModelDB:113649" - } - ], - "timestamp_created": "2024-01-11 15:28:00.506037+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/113649", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "781": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 781, - "name": "MEG of Somatosensory Neocortex (Jones et al. 2007)", - "repository_type": "github", - "summary": "\"... To make a direct and principled connection between the SI (somatosensory primary neocortex magnetoencephalography) waveform and underlying neural dynamics, we developed a biophysically realistic\r\ncomputational SI model that contained excitatory and inhibitory neurons in supragranular and infragranular layers. ... our model\r\nprovides a biophysically realistic solution to the MEG signal and can predict the electrophysiological correlates of human perception.\"\r\n", - "tags": [ - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 844, - "tag": "I R" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 851, - "tag": "Magnetoencephalography" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 764, - "tag": "Touch" - }, - { - "id": 1252, - "tag": "ModelDB:113732" - } - ], - "timestamp_created": "2024-01-11 15:28:01.015779+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/113732", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "782": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 782, - "name": "KInNeSS : a modular framework for computational neuroscience (Versace et al. 2008)", - "repository_type": "github", - "summary": "The xml files provided here implement a network of excitatory and inhibitory spiking neurons, governed by either Hodgkin-Huxley or quadratic integrate-and-fire dynamical equations. The code is used to demonstrate the capabilities of the KInNeSS software package for simulation of networks of spiking neurons. The simulation protocol used here is meant to facilitate the comparison of KInNeSS with other simulators reviewed in Brette et al. (2007). See the associated paper \"Versace et al. (2008) KInNeSS : a modular framework for computational neuroscience.\" for an extensive description of KInNeSS .", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 765, - "tag": "I Chloride" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 852, - "tag": "KInNeSS" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 640, - "tag": "NeuroML" - }, - { - "id": 1253, - "tag": "ModelDB:113939" - } - ], - "timestamp_created": "2024-01-11 15:28:01.534044+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/113939", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "783": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 783, - "name": "Dendritic processing of excitatory synaptic input in GnRH neurons (Roberts et al. 2006)", - "repository_type": "github", - "summary": "\"... we used electrophysiological recordings and neuronal reconstructions to generate computer models of (Gonadotopin-Releasing Hormone) GnRH neurons to examine the effects of synaptic inputs at varying distances from the soma along dendrites. ... analysis of reduced morphology models indicated that this population of cells is unlikely to exhibit low-frequency tonic spiking in the absence of synaptic input. ... applying realistic patterns of synaptic input to modeled GnRH neurons indicates that synapses located more than about 30% of the average dendrite length from the soma cannot drive firing at frequencies consistent with neuropeptide release. Thus, processing of synaptic input to dendrites of GnRH neurons is probably more complex than simple summation.\"\r\n", - "tags": [ - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 1254, - "tag": "ModelDB:113949" - } - ], - "timestamp_created": "2024-01-11 15:28:02.025880+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/113949", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "784": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 784, - "name": "Hodgkin-Huxley model of persistent activity in prefrontal cortex neurons (Winograd et al. 2008)", - "repository_type": "github", - "summary": "The paper demonstrate a form of graded persistent activity activated by hyperpolarization. This phenomenon is modeled based on a slow calcium regulation of Ih, similar to that introduced\r\nearlier for thalamic neurons (see Destexhe et al., J Neurophysiol. 1996). The only difference is that the calcium signal is here provided by the high-threshold calcium current (instead of the low-threshold calcium current in thalamic neurons).", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1255, - "tag": "ModelDB:113997" - } - ], - "timestamp_created": "2024-01-11 15:28:02.545568+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/113997", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "785": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 785, - "name": "Hippocampal basket cell gap junction network dynamics (Saraga et al. 2006)", - "repository_type": "github", - "summary": "2 cell network of hippocampal basket cells connected by gap junctions. Paper explores how distal gap junctions and active dendrites can tune network dynamics.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 754, - "tag": "Delay" - }, - { - "id": 786, - "tag": "Electrotonus" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 1256, - "tag": "ModelDB:114047" - } - ], - "timestamp_created": "2024-01-11 15:28:03.024817+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/114047", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "786": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 786, - "name": "Effect of slowly inactivating IKdr to delayed firing of action potentials (Wu et al. 2008)", - "repository_type": "github", - "summary": "\"The properties of slowly inactivating delayed-rectifier K+ current (IKdr) were investigated in NG108-15 neuronal cells differentiated with long-term exposure to dibutyryl cyclic AMP. \r\n...\r\nThe computer model, in which state-dependent inactivation of IKdr was incorporated, was also implemented to predict the firing behavior present in NG108-15 cells. \r\n...\r\nOur theoretical work and the experimental results led us to propose a pivotal role of slowly inactivating IKdr in delayed firing of APs in NG108-15 cells. The results also suggest that aconitine modulation of IKdr gating is an important molecular mechanism through which it can contribute to neuronal firing.\"", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 754, - "tag": "Delay" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1257, - "tag": "ModelDB:114108" - } - ], - "timestamp_created": "2024-01-11 15:28:03.491544+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/114108", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "787": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 787, - "name": "Systems-level modeling of neuronal circuits for leech swimming (Zheng et al. 2007)", - "repository_type": "github", - "summary": "\"This paper describes a mathematical model of the neuronal central pattern generator (CPG) that controls the rhythmic body motion of the swimming leech. The systems approach is employed to capture the neuronal dynamics essential for generating coordinated oscillations of cell membrane potentials by a simple CPG architecture with a minimal number of parameters. \r\n... parameter estimation leads to predictions regarding the synaptic coupling strength and intrinsic period gradient along the nerve cord, the latter of which agrees qualitatively with experimental observations.\"", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 1258, - "tag": "ModelDB:114230" - } - ], - "timestamp_created": "2024-01-11 15:28:03.968778+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/114230", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "788": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 788, - "name": "Brette-Gerstner model (Touboul and Brette 2008)", - "repository_type": "github", - "summary": "Brian code to simulate the Brette-Gerstner model and reproduce the figures of Touboul and Brette, Biol Cyber (2008).", - "tags": [ - { - "id": 645, - "tag": "Brian" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 1259, - "tag": "ModelDB:114242" - } - ], - "timestamp_created": "2024-01-11 15:28:04.434417+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/114242", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "789": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 789, - "name": "A comparative computer simulation of dendritic morphology (Donohue and Ascoli 2008)", - "repository_type": "github", - "summary": "Morphological aspects of dendritic branching such branch lengths, taper rates,ratios of daughter radii, and bifurcation probabilities are measured from real cells. These morphometrics are then resampled to create virtual trees based on the current branch order, radius, path distance to the soma, or combination of the three.", - "tags": [ - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 778, - "tag": "Java" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1260, - "tag": "ModelDB:114310" - } - ], - "timestamp_created": "2024-01-11 15:28:04.901367+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/114310", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "790": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 790, - "name": "Ionic mechanisms of bursting in CA3 pyramidal neurons (Xu and Clancy 2008)", - "repository_type": "github", - "summary": "\"... We present a single-compartment model of a CA3 hippocampal pyramidal neuron based on recent experimental data. We then use the model to determine the roles of primary depolarizing currents in burst generation.\r\nThe single compartment\r\nmodel incorporates accurate representations of sodium (Na+) channels (NaV1.1) and T-type calcium (Ca2+) channel subtypes\r\n(CaV3.1, CaV3.2, and CaV3.3).\r\nOur simulations predict the importance of Na+ and T-type Ca2+ channels in hippocampal\r\npyramidal cell bursting and reveal the distinct contribution of each subtype to burst morphology.\r\nWe also performed fastslow\r\nanalysis in a reduced comparable model, which shows that our model burst is generated as a result of the interaction\r\nof two slow variables, the T-type Ca2+ channel activation gate and the Ca2+-dependent potassium (K+) channel activation\r\ngate.\r\nThe model reproduces a range of experimentally observed phenomena including afterdepolarizing potentials, spike widening at the end of the burst, and rebound.\r\nFinally, we use the model to simulate the effects of two epilepsy-linked\r\nmutations: R1648H in NaV1.1 and C456S in CaV3.2, both of which result in increased cellular excitability.\"\r\n", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1261, - "tag": "ModelDB:114337" - } - ], - "timestamp_created": "2024-01-11 15:28:05.442018+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/114337", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "791": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 791, - "name": "Activity dependent changes in dendritic spine density and spine structure (Crook et al. 2007)", - "repository_type": "github", - "summary": "\"... In this work, we extend previous modeling studies [27] by combining a model for activity-dependent spine density with one for\r\ncalcium-mediated spine stem restructuring.\r\n... Additional equations characterize the change in spine density along\r\nthe dendrite, the current balance equation for an individual spine\r\nhead, the change in calcium concentration in the spine head, and the\r\ndynamics of spine stem resistance.\r\n\r\nWe use computational studies to investigate the changes in spine\r\ndensity and structure for differing synaptic inputs and demonstrate\r\nthe effects of these changes on the input-output properties of the\r\ndendritic branch.\r\n... \"", - "tags": [ - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 1262, - "tag": "ModelDB:114342" - } - ], - "timestamp_created": "2024-01-11 15:28:05.968845+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/114342", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "792": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 792, - "name": "Homeostatic synaptic plasticity (Rabinowitch and Segev 2006a,b)", - "repository_type": "github", - "summary": "(2006a): \"We investigated analytically and numerically the interplay between two opposing forms of synaptic plasticity: positive-feedback, long-term potentiation/depression (LTP/LTD), and negative-feedback, homeostatic synaptic plasticity (HSP). A detailed model of a CA1 pyramidal neuron, with numerous HSP-modifiable dendritic synapses, demonstrates that HSP may have an important role in selecting which spatial patterns of LTP/LTD are to last.\r\n...\r\nDespite the negative-feedback nature of HSP, under both local and global HSP, numerous synaptic \r\npotentiations/depressions can persist. These experimentally testable results imply that HSP could be significantly involved in shaping the spatial distribution of synaptic weights in the dendrites and not just normalizing it, as is currently believed.\"\r\n(2006b): \"Homeostatic synaptic plasticity (HSP) is an important mechanism attributed with the slow regulation of the neuron's activity. Whenever activity is chronically enhanced, HSP weakens the weights of the synapses in the dendrites and vice versa. Because dendritic morphology and its electrical properties partition the dendritic tree into functional compartments, we set out to explore the interplay between HSP and dendritic compartmentalization.\r\n...\r\nThe spatial distribution of synaptic weights throughout the dendrites will markedly differ under the local versus global HSP mechanisms. We suggest an experimental paradigm to unravel which type of HSP mechanism operates in the dendritic tree. The answer to this question will have important implications to our understanding of the functional organization of the neuron.\"\r\n", - "tags": [ - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 1263, - "tag": "ModelDB:114355" - } - ], - "timestamp_created": "2024-01-11 15:28:06.491354+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/114355", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "793": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 793, - "name": "Impact of dendritic size and topology on pyramidal cell burst firing (van Elburg and van Ooyen 2010)", - "repository_type": "github", - "summary": "The code provided here was written to systematically investigate which of the\r\nphysical parameters controlled by dendritic morphology underlies the differences\r\nin spiking behaviour observed in different realizations of the\r\n'ping-pong'-model. Structurally varying dendritic topology and length in a\r\nsimplified model allows us to separate out the physical parameters derived from\r\nmorphology underlying burst firing.\r\nTo perform the parameter scans we created a new NEURON tool the\r\nMultipleRunControl which can be used to easily set up a parameter scan and write\r\nthe simulation results to file.\r\nUsing this code we found that not input conductance but the arrival time of the\r\nreturn current, as measured provisionally by the average electrotonic path\r\nlength, determines whether the pyramidal cell (with ping-pong model dynamics)\r\nwill burst or fire single spikes.", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 1264, - "tag": "ModelDB:114359" - } - ], - "timestamp_created": "2024-01-11 15:28:06.997973+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/114359", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "794": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 794, - "name": "An allosteric kinetics of NMDARs in STDP (Urakubo et al. 2008)", - "repository_type": "github", - "summary": "\"... We developed a detailed biophysical model of STDP and found\r\nthat the model required spike timing-dependent distinct suppression of NMDARs by Ca2+-calmodulin.\r\n\r\nThis led us to predict an allosteric\r\nkinetics of NMDARs: a slow and rapid suppression of NMDARs by Ca2+-calmodulin with prespiking -> postspiking and postspiking -> prespiking, respectively.\r\n\r\nWe found that the allosteric kinetics, but not the conventional kinetics, is consistent with specific features of\r\namplitudes and peak time of NMDAR-mediated EPSPs in experiments.\r\n...\" See paper for more and details.", - "tags": [ - { - "id": 750, - "tag": "GENESIS (web link to model)" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 802, - "tag": "STDP" - }, - { - "id": 751, - "tag": "Signaling pathways" - }, - { - "id": 1265, - "tag": "ModelDB:114365" - } - ], - "timestamp_created": "2024-01-11 15:28:07.494477+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/114365", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "795": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 795, - "name": "Na+ channel dependence of AP initiation in cortical pyramidal neuron (Kole et al. 2008)", - "repository_type": "github", - "summary": "In this simulation action potential initiation, action potential properties and the role of axon initial segment Na+ channels are investigated in a realistic model of a layer 5 pyramidal neuron axon initial segment. The main Na+ channel properties were constrained by experimental data and the axon initial segment was reconstructed. Model parameters were constrained by direct recordings at the axon initial segment.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1266, - "tag": "ModelDB:114394" - } - ], - "timestamp_created": "2024-01-11 15:28:07.986288+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/114394", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "796": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 796, - "name": "A simplified model of NMDA oscillations in lamprey locomotor neurons (Huss et al. 2008)", - "repository_type": "github", - "summary": "Using experiments in conjunction with this simplified model, we sought to understand the basic mechanisms behind NMDA-induced oscillations in lamprey locomotor neurons, specifically (a) how the oscillation frequency depends on NMDA concentration and why, and (b) what the minimal number of components for generating NMDA oscillations is (in vitro and in the model).", - "tags": [ - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1267, - "tag": "ModelDB:114424" - } - ], - "timestamp_created": "2024-01-11 15:28:08.468617+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/114424", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "797": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 797, - "name": "Synergistic inhibitory action of oxcarbazepine on INa and IK (Huang et al. 2008)", - "repository_type": "github", - "summary": "\"Oxcarbazepine (OXC), one of the newer anti-epileptic drugs, has been demonstrating its efficacy on wide-spectrum neuropsychiatric disorders. \r\n... With the aid of patch-clamp technology, we first investigated the effects of OXC on ion currents in NG108-15 neuronal cells differentiated with cyclic AMP. \r\n\r\nWe found OXC ... caused a reversible reduction in the amplitude of voltage-gated Na+ current (INa) ...\r\nand produce(d) a significant prolongation in the recovery of INa inactivation. \r\n\r\n...\r\nMoreover, OXC could suppress the amplitude of delayed rectifier K+ current (IK(DR)), with no effect on M-type K+ current (IK(M)). \r\n\r\n...\r\nFurthermore, the simulations, based on hippocampal pyramidal neurons (Pinsky-Rinzel model) and a network of the Hodgkin-Huxley model, were analysed to investigate the effect of OXC on action potentials. \r\n\r\nTaken together, our results suggest that the synergistic blocking effects on INa and IK(DR) may contribute to the underlying mechanisms through which OXC affects neuronal function in vivo.\"\r\n", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1268, - "tag": "ModelDB:114450" - } - ], - "timestamp_created": "2024-01-11 15:28:08.954409+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/114450", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "798": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 798, - "name": "Efffect of propofol on potassium current in cardiac H9c2 cells (Liu et al. 2008)", - "repository_type": "github", - "summary": "\"...\r\nThe effects \r\nof propofol, an intravenous anesthetic agent with a distinct chemical structure, \r\non ion currents of differentiated clonal cardiac (H9c2) cells were investigated \r\nin this study. \r\n\r\nPropofol ... suppressed the amplitude of delayed \r\nrectifier K(+) current (I(K(DR))) in a concentration-dependent manner with \r\nan IC(50) value of 36 muM. \r\n...\r\nPropofol (30 muM) had no effect on erg-mediated K(+) \r\ncurrent in these cells; however, it suppressed L-type Ca(2+) current (I(Ca,L)) \r\nof cardiac and skeletal types to a similar extent. \r\n...\r\nNumerical \r\nsimulations of I(K(DR)) based on a Markovian model reproduce the \r\nexperimental results and show that propofol-induced blockade of I(K(DR)) \r\nis associated with an decrease in forward rate of the activation process and \r\nan increase in transitional rate into the inactivated state. \r\n\r\n...\"", - "tags": [ - { - "id": 737, - "tag": "Heart disease" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 783, - "tag": "I_Ks" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1269, - "tag": "ModelDB:114451" - } - ], - "timestamp_created": "2024-01-11 15:28:09.540493+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/114451", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "799": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 799, - "name": "CaMKII system exhibiting bistability with respect to calcium (Graupner and Brunel 2007)", - "repository_type": "github", - "summary": "\"... We present a detailed biochemical model of the CaMKII autophosphorylation and the protein signaling cascade governing the CaMKII dephosphorylation.\r\n... it is shown that the CaMKII system can qualitatively reproduce results of plasticity outcomes in response to spike-timing dependent plasticity (STDP) and presynaptic stimulation protocols.\r\nThis shows that the CaMKII protein network can account for both induction, through LTP/LTD-like transitions, and storage, due to its bistability, of synaptic changes.\"", - "tags": [ - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 751, - "tag": "Signaling pathways" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1270, - "tag": "ModelDB:114452" - } - ], - "timestamp_created": "2024-01-11 15:28:10.043527+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/114452", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "800": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 800, - "name": "Contrast invariance by LGN synaptic depression (Banitt et al. 2007)", - "repository_type": "github", - "summary": "\"Simple cells in layer 4 of the primary visual cortex of the cat show contrast-invariant orientation tuning, in which the amplitude of the\r\npeak response is proportional to the stimulus contrast but the width of the tuning curve hardly changes with contrast. \r\n\r\nThis study uses a\r\ndetailed model of spiny stellate cells (SSCs) from cat area 17 to explain this property. \r\n\r\nThe model integrates our experimental data,\r\nincluding morphological and intrinsic membrane properties and the number and spatial distribution of four major synaptic input\r\nsources of the SSC: the dorsal lateral geniculate nucleus (dLGN) and three cortical sources. \r\n\r\n...\r\n\r\nThe model response is in close\r\nagreement with experimental results, in terms of both output spikes and membrane voltage (amplitude and fluctuations), with reasonable\r\nexceptions given that recurrent connections were not incorporated.\"", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 719, - "tag": "Pattern Recognition" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 726, - "tag": "Vision" - }, - { - "id": 1271, - "tag": "ModelDB:114637" - } - ], - "timestamp_created": "2024-01-11 15:28:10.537899+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/114637", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "801": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 801, - "name": "Globus pallidus multi-compartmental model neuron with realistic morphology (Gunay et al. 2008)", - "repository_type": "github", - "summary": "\"Globus pallidus (GP) neurons recorded in brain slices show significant variability in intrinsic electrophysiological properties. \r\n\r\nTo investigate how this variability arises, we manipulated the biophysical properties of GP neurons using computer simulations. \r\n\r\n\r\n...\r\nOur results indicated that most of the experimental variability could be matched by varying conductance densities, which we confirmed with additional partial block experiments. \r\n\r\nFurther analysis resulted in two key observations: (1) each voltage-gated conductance had effects on multiple measures such as action potential waveform and spontaneous or stimulated spike rates; and (2) the effect of each conductance was highly dependent on the background context of other conductances present. \r\n\r\nIn some cases, such interactions could reverse the effect of the density of one conductance on important excitability measures. \r\n...\"", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 784, - "tag": "KCNQ1" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 1272, - "tag": "ModelDB:114639" - } - ], - "timestamp_created": "2024-01-11 15:28:11.099570+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/114639", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "802": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 802, - "name": "High-Res. Recordings Using a Real-Time Computational Model of the Electrode (Brette et al. 2008)", - "repository_type": "github", - "summary": "\"Intracellular recordings of neuronal membrane potential are a central tool in neurophysiology.\r\n...\r\nWe introduce a computer-aided technique, Active Electrode Compensation (AEC), based on a digital model of the electrode interfaced in real time with the electrophysiological setup. \r\n...\r\nAEC should be particularly useful to characterize fast neuronal phenomena intracellularly in vivo.\"", - "tags": [ - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 743, - "tag": "NEURON (web link to model)" - }, - { - "id": 853, - "tag": "Python (web link to model)" - }, - { - "id": 854, - "tag": "Scilab (web link to model)" - }, - { - "id": 1273, - "tag": "ModelDB:114643" - } - ], - "timestamp_created": "2024-01-11 15:28:11.606347+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/114643", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "803": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 803, - "name": "Axonal gap junctions produce fast oscillations in cerebellar Purkinje cells (Traub et al. 2008)", - "repository_type": "github", - "summary": "Examines how electrical coupling between proximal axons produces fast oscillations in cerebellar Purkinje cells.\r\nTraub RD, Middleton SJ, Knopfel T, Whittington MA (2008) Model of very fast (>75 Hz) network oscillations generated by electrical coupling between the proximal axons of cerebellar Purkinje cells. European Journal of Neuroscience.", - "tags": [ - { - "id": 710, - "tag": "FORTRAN" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 844, - "tag": "I R" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 742, - "tag": "I p,q" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 1274, - "tag": "ModelDB:114654" - } - ], - "timestamp_created": "2024-01-11 15:28:12.323305+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/114654", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "804": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 804, - "name": "Laminar connectivity matrix simulation (Weiler et al 2008)", - "repository_type": "github", - "summary": "A routine that simulates the flow of activity within and across laminar levels in the local pyramidal neuron network, based on a connectivity matrix (W) measured by laser scanning photostimulation in mouse somatic motor cortex, and a very simple neural network simulation.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 855, - "tag": "Connectivity matrix" - }, - { - "id": 856, - "tag": "Laminar Connectivity" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1275, - "tag": "ModelDB:114655" - } - ], - "timestamp_created": "2024-01-11 15:28:12.807780+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/114655", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "805": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 805, - "name": "Olfactory bulb mitral and granule cell column formation (Migliore et al. 2007)", - "repository_type": "github", - "summary": "In the olfactory bulb, the processing units for odor discrimination are believed \r\nto involve dendrodendritic synaptic interactions between mitral and granule cells. \r\nThere is increasing anatomical evidence that these cells are organized in columns, \r\nand that the columns processing a given odor are arranged in widely distributed arrays. \r\nExperimental evidence is lacking on the underlying learning mechanisms for how these \r\ncolumns and arrays are formed. \r\nWe have used a simplified realistic circuit model to test the hypothesis that \r\ndistributed connectivity can self-organize through an activity-dependent dendrodendritic \r\nsynaptic mechanism. \r\nThe results point to action potentials propagating in the mitral cell lateral dendrites \r\nas playing a critical role in this mechanism, and suggest a novel and robust learning \r\nmechanism for the development of distributed processing units in a cortical structure.\r\n", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 825, - "tag": "Learning" - }, - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 1276, - "tag": "ModelDB:114665" - } - ], - "timestamp_created": "2024-01-11 15:28:13.295100+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/114665", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "806": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 806, - "name": "GPi/GPe neuron models (Johnson and McIntyre 2008)", - "repository_type": "github", - "summary": "Model files for two types of non-human primate neurons used in the paper: simplified versions of 1) a GPi neuron and 2) a GPe axon collateralizing in GPi en route to STN.", - "tags": [ - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 734, - "tag": "Extracellular Fields" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 1277, - "tag": "ModelDB:114685" - } - ], - "timestamp_created": "2024-01-11 15:28:13.832170+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/114685", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "807": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 807, - "name": "High frequency oscillations induced in three gap-junction coupled neurons (Tseng et al. 2008)", - "repository_type": "github", - "summary": "Here we showed experimentally that high frequency oscillations (up to 600 Hz) were easily induced in a purely gap-junction coupled network by simple two stimuli with very short interval. The root cause is that the second elicited spike suffered from slow propagation speed and failure to transmit through a low-conductance junction. Similiar results were also obtained in these simulation.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 745, - "tag": "Conduction failure" - }, - { - "id": 754, - "tag": "Delay" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1278, - "tag": "ModelDB:114735" - } - ], - "timestamp_created": "2024-01-11 15:28:14.326536+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/114735", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "808": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 808, - "name": "Axonal NaV1.6 Sodium Channels in AP Initiation of CA1 Pyramidal Neurons (Royeck et al. 2008)", - "repository_type": "github", - "summary": "\"...\r\n\r\nWe show that the Na+ channel NaV1.6 displays a striking aggregation at the AIS\r\nof cortical neurons.\r\n\r\n\r\n...\r\nIn combination with simulations using a realistic\r\ncomputer model of a CA1 pyramidal cell, our results imply that a hyperpolarized\r\nvoltage-dependence of activation of AIS NaV1.6 channels is important both in\r\ndetermining spike threshold and localizing spike initiation to the AIS. \r\n\r\n...\r\nThese results suggest that NaV1.6 subunits at the AIS contribute significantly to\r\nits role as spike trigger zone and shape repetitive discharge properties of CA1 neurons.\"\r\n", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 843, - "tag": "I Q" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 742, - "tag": "I p,q" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 1279, - "tag": "ModelDB:115356" - } - ], - "timestamp_created": "2024-01-11 15:28:15.129189+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/115356", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "809": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 809, - "name": "Generalized Carnevale-Hines algorithm (van Elburg and van Ooyen 2009)", - "repository_type": "github", - "summary": "Demo illustrating the behaviour of the integrate-and-fire model in the parameter regime relevant for the generalized event-based Carnevale-Hines integration scheme. The demo includes the improved implementation of the IntFire4 mechanism.\r\n", - "tags": [ - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 1280, - "tag": "ModelDB:115357" - } - ], - "timestamp_created": "2024-01-11 15:28:15.672997+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/115357", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "810": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 810, - "name": "Human Attentional Networks: A Connectionist Model (Wang and Fan 2007)", - "repository_type": "github", - "summary": "\"... We describe a connectionist model of human attentional networks to explore the\r\npossible interplays among the networks from a computational\r\nperspective. This model is developed in the framework of\r\nleabra (local, error-driven, and associative, biologically realistic\r\nalgorithm) and simultaneously involves these attentional networks\r\nconnected in a biologically inspired way. ...\r\n\r\n\r\nWe evaluate the model by simulating the empirical data collected on normal human\r\nsubjects using the Attentional Network Test (ANT).\r\n\r\nThe simulation results fit the experimental data well.\r\n\r\nIn addition, we show that the same model, with a single parameter change that\r\naffects executive control, is able to simulate the empirical data collected\r\nfrom patients with schizophrenia.\r\n\r\nThis model represents a plausible connectionist explanation for the functional structure\r\nand interaction of human attentional networks.\"\r\n", - "tags": [ - { - "id": 771, - "tag": "Action Selection/Decision Making" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 807, - "tag": "Emergent/PDPplusplus" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 845, - "tag": "Schizophrenia" - }, - { - "id": 822, - "tag": "Winner-take-all" - }, - { - "id": 1281, - "tag": "ModelDB:115813" - } - ], - "timestamp_created": "2024-01-11 15:28:16.167138+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/115813", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "811": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 811, - "name": "Neural modeling of an internal clock (Yamazaki and Tanaka 2008)", - "repository_type": "github", - "summary": "\"We studied a simple random recurrent inhibitory network. Despite its simplicity, the dynamics was so rich that activity patterns of neurons\r\nevolved with time without recurrence due to random recurrent connections\r\namong neurons. The sequence of activity patterns was generated\r\nby the trigger of an external signal, and the generation was stable against\r\nnoise.... Therefore, a\r\ntime passage from the trigger of an external signal could be represented by\r\nthe sequence of activity patterns, suggesting that this model could work\r\nas an internal clock. ...\"", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 791, - "tag": "Rate-coding model neurons" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 1282, - "tag": "ModelDB:115966" - } - ], - "timestamp_created": "2024-01-11 15:28:16.687004+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/115966", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "812": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 812, - "name": "Polychronization: Computation With Spikes (Izhikevich 2005)", - "repository_type": "github", - "summary": "\"We present a minimal spiking network that can polychronize, that is,\r\nexhibit reproducible time-locked but not synchronous firing patterns\r\nwith millisecond precision, as in synfire braids. \r\n\r\nThe network consists\r\nof cortical spiking neurons with axonal conduction delays and spiketiming-\r\ndependent plasticity (STDP); a ready-to-use MATLAB code is\r\nincluded. \r\n\r\nIt exhibits sleeplike oscillations, gamma (40 Hz) rhythms,\r\nconversion of firing rates to spike timings, and other interesting regimes.\r\n\r\n...\r\nTo our surprise, the number of\r\ncoexisting polychronous groups far exceeds the number of neurons in\r\nthe network, resulting in an unprecedented memory capacity of the\r\nsystem. \r\n\r\n...\"", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 776, - "tag": "C or Cplusplus program (web link to model)" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 802, - "tag": "STDP" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 1283, - "tag": "ModelDB:115968" - } - ], - "timestamp_created": "2024-01-11 15:28:17.157478+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/115968", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "813": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 813, - "name": "Axonal Projection and Interneuron Types (Helmstaedter et al. 2008)", - "repository_type": "github", - "summary": "\"Interneurons in layer 2/3 (L2/3) of the somatosensory cortex show\r\n4 types of axonal projection patterns with reference to the laminae\r\nand borders of columns in rat barrel cortex (Helmstaedter et al.\r\n2008a). \r\n\r\nHere, we analyzed the dendritic geometry and electrical\r\nexcitability of these interneurons. \r\n\r\n...\r\nWe conclude that\r\n1) dendritic polarity is correlated to intrinsic electrical excitability,\r\nand 2) the axonal projection pattern represents an independent\r\nclassifier of interneurons.\r\n\"", - "tags": [ - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 743, - "tag": "NEURON (web link to model)" - }, - { - "id": 1284, - "tag": "ModelDB:116053" - } - ], - "timestamp_created": "2024-01-11 15:28:17.653776+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116053", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "814": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 814, - "name": "CA1 pyramidal neuron synaptic integration (Jarsky et al. 2005)", - "repository_type": "github", - "summary": "\"The perforant-path projection to the hippocampus forms synapses in the apical tuft of CA1 pyramidal neurons. \r\n\r\nWe used computer modeling to examine the function of these distal synaptic inputs, which led to three predictions that we confirmed in experiments using rat hippocampal slices. \r\n\r\n...\r\n\r\nThis 'gating' of dendritic spike propagation may be an important activation mode of CA1 pyramidal neurons, and its modulation by neurotransmitters or long-term, activity-dependent plasticity may be an important feature of dendritic integration during mnemonic processing in the hippocampus.\"\r\n", - "tags": [ - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 743, - "tag": "NEURON (web link to model)" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 1285, - "tag": "ModelDB:116084" - } - ], - "timestamp_created": "2024-01-11 15:28:18.127041+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116084", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "815": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 815, - "name": "Code to calc. spike-trig. ave (STA) conduct. from Vm (Pospischil et al. 2007, Rudolph et al. 2007)", - "repository_type": "github", - "summary": "PYTHON code to calculate spike-triggered average (STA) conductances from intracellular recordings, according to the method published by Pospischil et al., J Neurophysiol, 2007. The method consists of a maximum likelihood estimate of the conductance STA, from the voltage STA (which is calculated from the data). The method was tested using models and dynamic-clamp experiments; for details, see the original publication (Pospischil et al., 2007). The first application of this method to experimental data was from intracellular recordings in awake cat cerebral cortex (Rudolph et al., 2007).", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 592, - "tag": "Sleep" - }, - { - "id": 1286, - "tag": "ModelDB:116086" - } - ], - "timestamp_created": "2024-01-11 15:28:18.608994+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116086", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "816": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 816, - "name": "Lateral dendrodenditic inhibition in the Olfactory Bulb (David et al. 2008)", - "repository_type": "github", - "summary": "Mitral cells, the principal output neurons of the olfactory bulb, receive direct synaptic activation from primary sensory neurons. Shunting inhibitory inputs delivered by granule cell interneurons onto mitral cell lateral dendrites are believed to influence spike timing and underlie coordinated field potential oscillations. Lateral dendritic shunt conductances delayed spiking to a degree dependent on both their electrotonic distance and phase of onset. Recurrent inhibition significantly narrowed the distribution of mitral cell spike times, illustrating a tendency towards coordinated synchronous activity. This result suggests an essential role for early mechanisms of temporal coordination in olfaction. The model was adapted from Davison et al, 2003, but include additional noise mechanisms, long lateral dendrite, and specific synaptic point processes.", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 1287, - "tag": "ModelDB:116094" - } - ], - "timestamp_created": "2024-01-11 15:28:19.098859+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116094", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "817": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 817, - "name": "Biophysical and phenomenological models of spike-timing dependent plasticity (Badoual et al. 2006)", - "repository_type": "github", - "summary": "\"Spike-timing dependent plasticity (STDP) is a form of associative synaptic modification which depends\r\non the respective timing of pre- and post-synaptic spikes. \r\n\r\nThe biophysical mechanisms underlying this\r\nform of plasticity are currently not known. \r\n\r\nWe present here a biophysical model which captures the\r\ncharacteristics of STDP, such as its frequency dependency, and the effects of spike pair or spike triplet\r\ninteractions. \r\n...\r\nA simplified phenomenological\r\nmodel is also derived...\"", - "tags": [ - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 802, - "tag": "STDP" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 1288, - "tag": "ModelDB:116096" - } - ], - "timestamp_created": "2024-01-11 15:28:19.654327+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116096", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "818": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 818, - "name": "Dynamics of Spike Initiation (Prescott et al. 2008)", - "repository_type": "github", - "summary": "\"Transduction of graded synaptic input into trains of all-or-none action potentials (spikes) is a crucial step in neural coding.\r\n\r\nHodgkin identified three classes of neurons with qualitatively different analog-to-digital transduction properties. \r\n\r\nDespite widespread use of this classification scheme, a generalizable explanation of its biophysical basis has not been described. \r\n\r\nWe recorded from spinal sensory neurons representing each class and reproduced their transduction properties in a minimal\r\nmodel. With phase plane and bifurcation analysis, each class of excitability was shown to derive from distinct spike initiating dynamics. \r\n\r\nExcitability could be converted between all three classes by varying single parameters; moreover, several\r\nparameters, when varied one at a time, had functionally equivalent effects on excitability. From this, we conclude that the\r\nspike-initiating dynamics associated with each of Hodgkin\u2019s classes represent different outcomes in a nonlinear competition\r\nbetween oppositely directed, kinetically mismatched currents. ...\"", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 804, - "tag": "Bifurcation" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 821, - "tag": "Sensory coding" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1289, - "tag": "ModelDB:116123" - } - ], - "timestamp_created": "2024-01-11 15:28:20.176706+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116123", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "819": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 819, - "name": "Multimodal stimuli learning in hawkmoths (Balkenius et al. 2008)", - "repository_type": "github", - "summary": "The moth Macroglossum stellatarum can learn the color and sometimes the odor of a rewarding food\r\nsource.\r\n\r\nWe present data from 20 different experiments with different combinations of blue and yellow\r\nartificial flowers and the two odors, honeysuckle and lavender. \r\n\r\n... \r\nThree computational models were tested in the same experimental situations as the real\r\nmoths and their predictions were compared with the experimental data. \r\n\r\n...\r\nNeither the\r\nRescorla\u2013Wagner model nor a learning model with independent learning for each stimulus component\r\nwere able to explain the experimental data. \r\n\r\nWe present the new hawkmoth learning model, which\r\nassumes that the moth learns a template for the sensory attributes of the rewarding stimulus. \r\n\r\nThis\r\nmodel produces behavior that closely matches that of the real moth in all 20 experiments.", - "tags": [ - { - "id": 771, - "tag": "Action Selection/Decision Making" - }, - { - "id": 776, - "tag": "C or Cplusplus program (web link to model)" - }, - { - "id": 825, - "tag": "Learning" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 1290, - "tag": "ModelDB:116312" - } - ], - "timestamp_created": "2024-01-11 15:28:20.799440+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116312", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "820": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 820, - "name": "A Model of Selection between Stimulus and Place Strategy in a Hawkmoth (Balkenius et al. 2004)", - "repository_type": "github", - "summary": "\"In behavioral experiments, the hawkmoth Deilephila elpenor can learn both the \r\ncolor and the position of artificial flowers.\r\n\r\n...\r\n\r\nWe show how a computational model can reproduce the behavior in the experimental situation.\r\n\r\nThe aim of the model is to investigate which learning and behavior selection strategies are \r\nnecessary to reproduce the behavior observed in the experiment.\r\n\r\nThe model is based on behavioral data and the sensitivities of the moth photoreceptors. \r\n\r\nThe model consists of a number of interacting behavior systems that are triggered by \r\nspecific stimuli and control specific behaviors. \r\n\r\nThe ability of the moth to learn the colors of different flowers and the adaptive processes involved \r\nin the choice between stimulus-approach and place-approach strategies are reproduced very accurately by the model. \r\nThe model has implications both for further studies of the ecology of the animal and for robotic systems.\"\r\n", - "tags": [ - { - "id": 776, - "tag": "C or Cplusplus program (web link to model)" - }, - { - "id": 825, - "tag": "Learning" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1291, - "tag": "ModelDB:116313" - } - ], - "timestamp_created": "2024-01-11 15:28:21.285020+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116313", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "821": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 821, - "name": "Pyramidal neurons switch from integrators to resonators (Prescott et al. 2008)", - "repository_type": "github", - "summary": "During wakefulness, pyramidal neurons in the intact brain are bombarded by synaptic\r\ninput that causes tonic depolarization, increased membrane conductance (i.e. shunting), and\r\nnoisy fluctuations in membrane potential; by comparison, pyramidal neurons in acute slices\r\ntypically experience little background input.\r\n\r\nSuch differences in operating conditions can compromise extrapolation of in vitro data to explain \r\nneuronal operation in vivo.\r\n\r\n...\r\n\r\n in slice experiments, we show that\r\nCA1 hippocampal pyramidal cells switch from integrators to resonators, i.e. from class 1 to class\r\n2 excitability.\r\n\r\nThe switch is explained by increased outward current contributed by the M-type\r\npotassium current IM\r\n...\r\nThus, even so-called \u201cintrinsic\u201d properties may differ qualitatively between in vitro and in vivo conditions.", - "tags": [ - { - "id": 804, - "tag": "Bifurcation" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1292, - "tag": "ModelDB:116386" - } - ], - "timestamp_created": "2024-01-11 15:28:21.746285+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116386", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "822": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 822, - "name": "NEURON + Python (Hines et al. 2009)", - "repository_type": "github", - "summary": "The NEURON simulation program now allows Python to be used alone or in\r\ncombination with NEURON's traditional Hoc interpreter. Adding Python to\r\nNEURON has the immediate benefit of making available a very extensive\r\nsuite of analysis tools written for engineering and science. It also\r\ncatalyzes NEURON software development by offering users a modern programming\r\ntool that is recognized for its flexibility and power to create and \r\nmaintain complex programs. At the same time, nothing is lost because\r\nall existing models written in Hoc, including GUI tools, continue to\r\nwork without change and are also available within the Python context.\r\nAn example of the benefits of Python availability is the use of the xml\r\nmodule in implementing NEURON's Import3D and CellBuild tools to read MorphML and\r\nNeuroML model specifications.", - "tags": [ - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 640, - "tag": "NeuroML" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 1293, - "tag": "ModelDB:116491" - } - ], - "timestamp_created": "2024-01-11 15:28:22.225588+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116491", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "823": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 823, - "name": "Modulation of septo-hippocampal theta activity by GABAA receptors (Hajos et al. 2004)", - "repository_type": "github", - "summary": "Theta frequency oscillation of the septo-hippocampal system has been considered as a prominent activity associated with cognitive function and affective processes.\r\n\r\n...\r\n\r\nIn the present experiments we applied a combination of computational and physiological techniques to explore the functional role of GABAA receptors in theta oscillation.\r\n\r\n...\r\n\r\nIn parallel to these experimental observations, a computational model has been constructed by implementing a septal GABA neuron model with a CA1 hippocampal model containing three types of neurons (including oriens and basket interneurons and pyramidal cells; latter modeled by multicompartmental techniques; for detailed model description with network parameters see online addendum: http://geza.kzoo.edu/theta).\r\n\r\nThis connectivity made the network capable of simulating the responses of the septo-hippocampal circuitry to the modulation of GABAA transmission, and the presently described computational model proved suitable to reveal several aspects of pharmacological modulation of GABAA receptors.\r\n\r\n In addition, computational findings indicated different roles of distinctively located GABAA receptors in theta generation.", - "tags": [ - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 1294, - "tag": "ModelDB:116567" - } - ], - "timestamp_created": "2024-01-11 15:28:22.727869+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116567", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "824": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 824, - "name": "A Model of Multiple Spike Initiation Zones in the Leech C-interneuron (Crisp 2009)", - "repository_type": "github", - "summary": "The leech C-interneuron and its electrical synapse with the S-interneuron exhibit unusual properties: an asymmetric delay when impulses travel from one soma to the other, and graded C-interneuron impulse amplitudes under elevated divalent cation concentrations. These properties have been simulated using a SNNAP model in which the C-interneuron has multiple, independent spike initiation zones associated with individual electrical junctions with the C-interneuron.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 757, - "tag": "SNNAP" - }, - { - "id": 1295, - "tag": "ModelDB:116575" - } - ], - "timestamp_created": "2024-01-11 15:28:23.231977+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116575", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "825": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 825, - "name": "Currents contributing to decision making in neurons B31-B32 of Aplysia (Hurwitz et al. 2008)", - "repository_type": "github", - "summary": "\"Biophysical properties of neurons contributing to the ability of an animal to decide whether or not to respond were examined. \r\n\r\nB31/B32, two pairs of bilaterally symmetrical Aplysia neurons, are major participants in deciding to initiate a buccal motor program, the neural correlate of a consummatory feeding response. \r\n\r\nB31/B32 respond to an adequate stimulus after a delay, during which time additional stimuli influence the decision to respond. \r\n\r\nB31/B32 then respond with a ramp depolarization followed by a sustained soma depolarization and axon spiking that is the expression of a commitment to respond to food. \r\n\r\nFour currents contributing to decision making in B31/B32 were characterized, and their functional effects were determined, in current- and voltage-clamp experiments and with simulations. ...\r\n\r\nHodgkin-Huxley kinetic analyses were performed on the outward currents. \r\n\r\nSimulations using equations from these analyses showed that IK-V and IK-A slow the ramp depolarization preceding the sustained depolarization. \r\n\r\nThe three outward currents contribute to braking the B31/B32 depolarization and keeping the sustained depolarization at a constant voltage. \r\n\r\nThe currents identified are sufficient to explain the properties of B31/B32 that play a role in generating the decision to feed.\"", - "tags": [ - { - "id": 771, - "tag": "Action Selection/Decision Making" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 757, - "tag": "SNNAP" - }, - { - "id": 1296, - "tag": "ModelDB:116606" - } - ], - "timestamp_created": "2024-01-11 15:28:23.766016+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116606", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "826": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 826, - "name": "Dentate gyrus granule cell: calcium and calcium-dependent conductances (Aradi and Holmes 1999)", - "repository_type": "github", - "summary": "We have constructed a detailed model of a hippocampal dentate granule (DG) cell that includes nine different channel types. \r\nChannel densities and distributions were chosen to reproduce reported physiological responses observed in normal solution and when blockers were applied. \r\n\r\nThe model was used to explore the contribution of each channel type to spiking behavior with particular emphasis on the mechanisms underlying postspike events. \r\n\r\n...\r\nThe model was used to predict changes in channel densities that could lead to epileptogenic burst discharges and to predict the effect of altered buffering capacity on firing behavior. \r\n\r\nWe conclude that the clustered spatial distributions of calcium related channels, the presence of slow delayed rectifier potassium currents in dendrites, and calcium buffering properties, together, might explain the resistance of DG cells to the development of epileptogenic burst discharges.", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1297, - "tag": "ModelDB:116740" - } - ], - "timestamp_created": "2024-01-11 15:28:24.253034+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116740", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "827": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 827, - "name": "Spine neck plasticity controls postsynaptic calcium signals (Grunditz et al. 2008)", - "repository_type": "github", - "summary": "This model was set up to dissect the relative contribution of different channels to \r\nthe spine calcium transients measured at single spines.\r\n", - "tags": [ - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 844, - "tag": "I R" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1298, - "tag": "ModelDB:116769" - } - ], - "timestamp_created": "2024-01-11 15:28:24.738523+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116769", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "828": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 828, - "name": "Robust Reservoir Generation by Correlation-Based Learning (Yamazaki & Tanaka 2008)", - "repository_type": "github", - "summary": "\"Reservoir computing (RC) is a new framework for neural computation. \r\nA reservoir is usually a recurrent neural network with fixed random connections. \r\n\r\nIn this article, we propose an RC model in which the connections in the reservoir are modifiable. \r\n\r\n...\r\nWe apply our RC model to trace eyeblink conditioning. \r\n\r\nThe reservoir bridged the gap of an interstimulus interval between the conditioned and unconditioned stimuli, and a readout neuron was able to learn and express the timed conditioned response.\"\r\n", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 825, - "tag": "Learning" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 791, - "tag": "Rate-coding model neurons" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 1299, - "tag": "ModelDB:116806" - } - ], - "timestamp_created": "2024-01-11 15:28:25.209775+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116806", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "829": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 829, - "name": "Broadening of activity with flow across neural structures (Lytton et al. 2008)", - "repository_type": "github", - "summary": "\"Synfire chains have long been suggested as a substrate for perception and information processing in the nervous system. \r\n\r\nHowever, embedding activation chains in a densely connected nervous matrix risks spread of signal that will obscure or obliterate the message. \r\n\r\nWe used computer modeling and physiological measurements in rat hippocampus to assess this problem of activity broadening.\r\n\r\nWe simulated a series of neural modules with feedforward propagation and random connectivity within each module and from one module to the next. ...\"", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 1300, - "tag": "ModelDB:116830" - } - ], - "timestamp_created": "2024-01-11 15:28:25.688329+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116830", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "830": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 830, - "name": "Multicompartmental cerebellar granule cell model (Diwakar et al. 2009)", - "repository_type": "github", - "summary": "A detailed multicompartmental model was used to study neuronal electroresponsiveness of cerebellar granule cells in rats. Here we show that, in cerebellar granule cells, Na+ channels are enriched in the axon, especially in the hillock, but almost absent from soma and dendrites. Numerical simulations indicated that granule cells have a compact electrotonic structure allowing EPSPs to diffuse with little attenuation from dendrites to axon. The spike arose almost simultaneously along the whole axonal ascending branch and invaded the hillock, whose activation promoted spike back-propagation with marginal delay (<200 micros) and attenuation (<20 mV) into the somato-dendritic compartment. For details check the cited article.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 763, - "tag": "Intrinsic plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1301, - "tag": "ModelDB:116835" - } - ], - "timestamp_created": "2024-01-11 15:28:26.165940+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116835", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "831": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 831, - "name": "Reward modulated STDP (Legenstein et al. 2008)", - "repository_type": "github", - "summary": "\"...\r\nThis article provides tools for an analytic treatment of reward-modulated\r\nSTDP, which allows us to predict under which conditions reward-modulated STDP will achieve a desired learning\r\neffect.\r\n\r\nThese analytical results imply that neurons can learn through reward-modulated STDP to classify not only spatial but\r\nalso temporal firing patterns of presynaptic neurons. \r\n\r\nThey also can learn to respond to specific presynaptic firing patterns\r\nwith particular spike patterns. \r\n\r\nFinally, the resulting learning theory predicts that even difficult credit-assignment problems,\r\nwhere it is very hard to tell which synaptic weights should be modified in order to increase the global reward for the system,\r\ncan be solved in a self-organizing manner through reward-modulated STDP. \r\n\r\nThis yields an explanation for a fundamental\r\nexperimental result on biofeedback in monkeys by Fetz and Baker. \r\n\r\nIn this experiment monkeys were rewarded for\r\nincreasing the firing rate of a particular neuron in the cortex and were able to solve this extremely difficult credit assignment\r\nproblem. \r\n...\r\nIn addition our model\r\ndemonstrates that reward-modulated STDP can be applied to all synapses in a large recurrent neural network without\r\nendangering the stability of the network dynamics.\"", - "tags": [ - { - "id": 857, - "tag": "Biofeedback" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 858, - "tag": "PCSIM" - }, - { - "id": 719, - "tag": "Pattern Recognition" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 809, - "tag": "Reinforcement Learning" - }, - { - "id": 859, - "tag": "Reward-modulated STDP" - }, - { - "id": 802, - "tag": "STDP" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 1302, - "tag": "ModelDB:116837" - } - ], - "timestamp_created": "2024-01-11 15:28:26.643847+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116837", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "832": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 832, - "name": "The virtual slice setup (Lytton et al. 2008)", - "repository_type": "github", - "summary": "\"In an effort to design a simulation environment that is more similar to that of neurophysiology, we introduce a virtual slice setup in the NEURON simulator. \r\n\r\nThe virtual slice setup runs continuously and permits parameter changes, including changes to synaptic weights and time course and to intrinsic cell properties. \r\n\r\nThe virtual slice setup permits shocks to be applied at chosen locations and activity to be sampled intra- or extracellularly from chosen locations. ...\"", - "tags": [ - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1303, - "tag": "ModelDB:116838" - } - ], - "timestamp_created": "2024-01-11 15:28:27.124524+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116838", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "833": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 833, - "name": "Thalamic interneuron multicompartment model (Zhu et al. 1999)", - "repository_type": "github", - "summary": "This is an attempt to recreate a set of simulations originally performed in 1994 under NEURON version 3 and last tested in 1999. When I ran it now it did not behave exactly the same as previously which I suspect is due to some minor mod file changes on my side rather than due to any differences among versions. \r\n\r\nAfter playing around with the parameters a little bit I was able to get something that looks generally like a physiological trace in \r\nJ Neurophysiol, 81:702--711, 1999, fig. 8b top trace. \r\n\r\nThis sad preface is simply offered in order to encourage anyone who is interested in this model to make and post fixes. I'm happy to help out.\r\n\r\nSimulation by JJ Zhu\r\n\r\nTo run\r\nnrnivmodl\r\nnrngui.hoc\r\n\r\n", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 731, - "tag": "I CAN" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 1304, - "tag": "ModelDB:116862" - } - ], - "timestamp_created": "2024-01-11 15:28:27.621324+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116862", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "834": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 834, - "name": "High frequency stimulation of the Subthalamic Nucleus (Rubin and Terman 2004)", - "repository_type": "github", - "summary": "\" ... Using a computational model, this paper considers the hypothesis that DBS works by replacing pathologically rhythmic\r\nbasal ganglia output with tonic, high frequency firing.\r\n\r\nIn our simulations of parkinsonian conditions, rhythmic inhibition from GPi to the thalamus compromises the ability of thalamocortical relay (TC) cells to respond to depolarizing inputs, such as sensorimotor signals.\r\n\r\nHigh frequency stimulation of STN regularizes GPi firing, and this restores TC responsiveness, despite the increased frequency and amplitude of GPi inhibition to thalamus that result.\r\n\r\nWe provide a mathematical phase plane analysis of the mechanisms that determine TC relay capabilities in\r\nnormal, parkinsonian, and DBS states in a reduced model.\r\n\r\nThis analysis highlights the differences in deinactivation of the low-threshold calcium T -current that we observe in TC cells in these different conditions. ...\"\r\n", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 831, - "tag": "Deep brain stimulation" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1305, - "tag": "ModelDB:116867" - } - ], - "timestamp_created": "2024-01-11 15:28:28.176213+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116867", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "835": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 835, - "name": "Python demo of the VmT method to extract conductances from single Vm traces (Pospischil et al. 2009)", - "repository_type": "github", - "summary": "This python code implements a method to estimate synaptic conductances from single membrane potential traces (the \"VmT method\"), as described in Pospischil et al. (2009). The method uses a maximum likelihood procedure and was successfully tested using models and dynamic-clamp experiments in vitro (see paper for details).", - "tags": [ - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 1306, - "tag": "ModelDB:116870" - } - ], - "timestamp_created": "2024-01-11 15:28:28.687380+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116870", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "836": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 836, - "name": "A spiking model of cortical broadcast and competition (Shanahan 2008)", - "repository_type": "github", - "summary": "\"This paper presents a computer model of cortical broadcast and competition based on spiking neurons and inspired by\r\nthe hypothesis of a global neuronal workspace underlying conscious information processing in the human brain. In the\r\nmodel, the hypothesised workspace is realised by a collection of recurrently interconnected regions capable of sustaining\r\nand disseminating a reverberating spatial pattern of activation. ...\"", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 794, - "tag": "Working memory" - }, - { - "id": 1307, - "tag": "ModelDB:116871" - } - ], - "timestamp_created": "2024-01-11 15:28:29.170693+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116871", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "837": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 837, - "name": "Synaptic plasticity can produce and enhance direction selectivity (Carver et al, 2008)", - "repository_type": "github", - "summary": "\" ... We propose a parsimonious model of motion processing that generates direction selective responses using short-term synaptic depression and can reproduce salient features of direction selectivity found in a population of neurons in the midbrain of the weakly electric fish Eigenmannia virescens. \r\n\r\nThe model achieves direction selectivity with an elementary Reichardt motion detector: information from spatially separated receptive fields converges onto a neuron via dynamically different pathways. \r\n\r\nIn the model, these differences arise from convergence of information through distinct synapses that either exhibit or do not exhibit short-term synaptic depression\u2014short-term depression produces phase-advances relative to nondepressing synapses. ...\"\r\n", - "tags": [ - { - "id": 722, - "tag": "Depression" - }, - { - "id": 860, - "tag": "Direction Selectivity" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 768, - "tag": "Synaptic Convergence" - }, - { - "id": 1308, - "tag": "ModelDB:116901" - } - ], - "timestamp_created": "2024-01-11 15:28:29.711826+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116901", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "838": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 838, - "name": "Spike frequency adaptation in the LGMD (Peron and Gabbiani 2009)", - "repository_type": "github", - "summary": "This model is used in the referenced paper to demonstrate that a model of an SK-like calcium-sensitive potassium (KCa) conductance can replicate the spike frequency adaptation (SFA) of the locust lobula giant movement detector (LGMD) neuron. The model simulates current injection experiments with and without KCa block in the LGMD, as well as visual stimulation experiments with and without KCa block.", - "tags": [ - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 842, - "tag": "I Krp" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 777, - "tag": "Spike Frequency Adaptation" - }, - { - "id": 726, - "tag": "Vision" - }, - { - "id": 1309, - "tag": "ModelDB:116945" - } - ], - "timestamp_created": "2024-01-11 15:28:30.184499+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116945", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "839": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 839, - "name": "Fly lobular plate VS cell (Borst and Haag 1996, et al. 1997, et al. 1999)", - "repository_type": "github", - "summary": "In a series of papers the authors conducted experiments to develop understanding and models of fly visual system HS, CS, and VS neurons. This model recreates the VS neurons from those papers with enough success to merit approval by Borst although some discrepancies remain (see readme).", - "tags": [ - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 861, - "tag": "I_K,Na" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 862, - "tag": "Reliability" - }, - { - "id": 726, - "tag": "Vision" - }, - { - "id": 1310, - "tag": "ModelDB:116956" - } - ], - "timestamp_created": "2024-01-11 15:28:30.832683+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116956", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "840": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 840, - "name": "Data-driven, HH-type model of the lateral pyloric (LP) cell in the STG (Nowotny et al. 2008)", - "repository_type": "github", - "summary": "This model was developed using voltage clamp data and existing LP models to assemble an initial set of currents which were then adjusted by extensive fitting to a long data set of an isolated LP neuron. The main points of the work are\r\na) automatic fitting is difficult but works when the method is carefully adjusted to the problem (and the initial guess is good enough).\r\nb) The resulting model (in this case) made reasonable predictions for manipulations not included in the original data set, e.g., blocking some of the ionic currents.\r\nc) The model is reasonably robust against changes in parameters but the different parameters vary a lot in this respect.\r\nd) The model is suitable for use in a network and has been used for this purpose (Ivanchenko et al. 2008)", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 863, - "tag": "Parameter sensitivity" - }, - { - "id": 1311, - "tag": "ModelDB:116957" - } - ], - "timestamp_created": "2024-01-11 15:28:31.336380+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116957", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "841": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 841, - "name": "Effects of synaptic location and timing on synaptic integration (Rall 1964)", - "repository_type": "github", - "summary": "Reproduces figures 5 - 8 from \r\nRall, W.\r\nTheoretical significance of dendritic trees for neuronal input-output relations.\r\nIn: Neural Theory and Modeling, ed. Reiss, R.F., Palo Alto: Stanford University Press (1964).", - "tags": [ - { - "id": 786, - "tag": "Electrotonus" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 1312, - "tag": "ModelDB:116981" - } - ], - "timestamp_created": "2024-01-11 15:28:31.813696+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116981", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "842": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 842, - "name": "CA1 pyramidal neuron: h channel-dependent deficit of theta oscill. resonance (Marcelin et al. 2008)", - "repository_type": "github", - "summary": "This model was used to confirm and support experimental data\r\nsuggesting that the neuronal/circuitry changes associated with temporal lobe epilepsy,\r\nincluding Ih-dependent inductive mechanisms, can disrupt hippocampal theta function.\r\n", - "tags": [ - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 1313, - "tag": "ModelDB:116983" - } - ], - "timestamp_created": "2024-01-11 15:28:32.283423+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/116983", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "843": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 843, - "name": "Working memory circuit with branched dendrites (Morita 2008)", - "repository_type": "github", - "summary": "This is a rate-coding model of the neocortical spatial working memory circuit incorporating multiple dendritic branches of the individual pyramidal cell in order to examine how nonlinear dendritic integration, combined with the nonuniform distribution of the external input, affects the behavior of the whole circuit.", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 794, - "tag": "Working memory" - }, - { - "id": 1314, - "tag": "ModelDB:117204" - } - ], - "timestamp_created": "2024-01-11 15:28:32.759211+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/117204", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "844": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 844, - "name": "Excitability of PFC Basal Dendrites (Acker and Antic 2009)", - "repository_type": "github", - "summary": "\".. We\r\ncarried out multi-site voltage-sensitive dye imaging of membrane potential transients from thin basal\r\nbranches of prefrontal cortical pyramidal neurons before and after application of channel blockers. We\r\nfound that backpropagating action potentials (bAPs) are predominantly controlled by voltage-gated\r\nsodium and A-type potassium channels. In contrast, pharmacologically blocking the delayed rectifier\r\npotassium, voltage-gated calcium or Ih, conductance had little effect on dendritic action potential\r\npropagation. Optically recorded bAP waveforms were quantified and multicompartmental modeling\r\n(NEURON) was used to link the observed behavior with the underlying biophysical properties. The\r\nbest-fit model included a non-uniform sodium channel distribution with decreasing conductance with\r\ndistance from the soma, together with a non-uniform (increasing) A-type potassium conductance. AP\r\namplitudes decline with distance in this model, but to a lesser extent than previously thought. We used\r\nthis model to explore the mechanisms underlying two sets of published data involving high frequency\r\ntrains of action potentials, and the local generation of sodium spikelets. ...\"", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 1315, - "tag": "ModelDB:117207" - } - ], - "timestamp_created": "2024-01-11 15:28:33.240461+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/117207", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "845": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 845, - "name": "Hodgkin-Huxley simplifed 2D and 3D models (Lundstrom et al. 2009)", - "repository_type": "github", - "summary": "\"Neuronal responses are often characterized by the\r\nfiring rate as a function of the stimulus mean, or the f\u2013I curve.\r\n\r\nWe introduce a novel classification of neurons into Types A,\r\nB−, and B+ according to how f\u2013I curves are modulated by\r\ninput fluctuations. ...\"\r\n", - "tags": [ - { - "id": 804, - "tag": "Bifurcation" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1316, - "tag": "ModelDB:117330" - } - ], - "timestamp_created": "2024-01-11 15:28:33.779886+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/117330", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "846": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 846, - "name": "Quantal neurotransmitter release kinetics with fixed and mobile Ca2+ buffers (Gilmanov et al. 2008)", - "repository_type": "github", - "summary": "\"... In this work, we used computer modeling of\r\nquanta release kinetics with various levels of calcium influx\r\nand in the presence of endogenous calcium buffers with\r\nvarying mobilities. \r\n\r\nThe results of this modeling revealed\r\nthe desynchronization of quanta release under low calcium\r\ninflux in the presence of an endogenous fixed calcium\r\nbuffer, with a diffusion coefficient much smaller than that\r\nof free Ca2+, and synchronization occurred upon adding a\r\nmobile buffer. This corresponds to changes in secretion\r\ntime course parameters found experimentally ...\"\r\n", - "tags": [ - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 864, - "tag": "Pascal (web link to model)" - }, - { - "id": 1317, - "tag": "ModelDB:117351" - } - ], - "timestamp_created": "2024-01-11 15:28:34.247392+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/117351", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "847": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 847, - "name": "Numerical Integration of Izhikevich and HH model neurons (Stewart and Bair 2009)", - "repository_type": "github", - "summary": "The Parker-Sochacki method is a new technique for the numerical integration of differential equations applicable to many neuronal models.\r\nUsing this method, the solution order can be adapted according to the local conditions at each time step, enabling adaptive error control without changing the integration timestep. \r\nWe apply the Parker-Sochacki method to the Izhikevich \u2018simple\u2019 model and a Hodgkin-Huxley\r\ntype neuron, comparing the results with those obtained using the Runge-Kutta and Bulirsch-Stoer methods.", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 1318, - "tag": "ModelDB:117361" - } - ], - "timestamp_created": "2024-01-11 15:28:34.719010+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/117361", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "848": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 848, - "name": "Competition for AP initiation sites in a circuit controlling simple learning (Cruz et al. 2007)", - "repository_type": "github", - "summary": "\"The spatial and temporal patterns of action potential initiations were studied in a behaving leech preparation to determine the basis of increased firing that accompanies sensitization, a form of non-associative learning requiring the\r\nS-interneurons.\r\n\r\n...\r\nThe S-interneurons, one in each ganglion and linked by electrical synapses with both neighbors to form a chain, are interposed between sensory\r\nand motor neurons.\r\n\r\n...\r\n the single site with the largest initiation rate, the S-cell in the\r\nstimulated segment, suppressed initiations in adjacent ganglia.\r\n\r\nExperiments showed this was both because (1) it received the earliest, greatest input and (2) the delayed synaptic\r\ninput to the adjacent S-cells coincided with the action potential refractory period.\r\n\r\nA compartmental model of the S-cell and its inputs showed that a simple, intrinsic mechanism of inexcitability after each action potential may account for suppression of impulse initiations.\r\n\r\nThus, a non-synaptic competition between neurons alters synaptic integration in the chain.\r\n\r\nIn one mode, inputs to different sites sum independently, whereas in another, synaptic input to a single site precisely specifies the overall pattern of activity.\"\r\n", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 1319, - "tag": "ModelDB:117459" - } - ], - "timestamp_created": "2024-01-11 15:28:35.201487+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/117459", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "849": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 849, - "name": "Rat alpha7 nAChR desensitization is modulated by W55 (Gay et al. 2008)", - "repository_type": "github", - "summary": "\"The rat alpha7 nicotinic acetylcholine receptor (nAChR) can undergo rapid onset of desensitization; however, the mechanisms of desensitization are largely unknown. \r\n\r\nThe contribution of a tryptophan (W) residue at position 55 of the rat alpha7 nAChR subunit, which lies within the beta2 strand, was studied by mutating it to other hydrophobic and/or aromatic amino acids, followed by voltage-clamp experiments in Xenopus oocytes. \r\n\r\nWhen mutated to alanine, the alpha7-W55A nAChR desensitized more slowly, and recovered from desensitization more rapidly, than wildtype alpha7 nAChRs. \r\n\r\nThe contribution of desensitization was validated by kinetic modelling. ...\"", - "tags": [ - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 865, - "tag": "Pascal/Delphi" - }, - { - "id": 1320, - "tag": "ModelDB:117508" - } - ], - "timestamp_created": "2024-01-11 15:28:35.682981+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/117508", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "850": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 850, - "name": "Reverse-time correlation analysis for idealized orientation tuning dynamics (Kovacic et al. 2008)", - "repository_type": "github", - "summary": "\"A theoretical analysis is presented of a\r\nreverse-time correlation method used in experimentally\r\ninvestigating orientation tuning dynamics of\r\nneurons in the primary visual cortex.\r\n\r\nAn exact mathematical\r\ncharacterization of the method is developed,\r\nand its connection with the Volterra\u2013Wiener nonlinear\r\nsystems theory is described.\r\n\r\nVarious mathematical\r\nconsequences and possible physiological implications\r\nof this analysis are illustrated using exactly solvable\r\nidealized models of orientation tuning.\"", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 726, - "tag": "Vision" - }, - { - "id": 1321, - "tag": "ModelDB:117514" - } - ], - "timestamp_created": "2024-01-11 15:28:36.143632+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/117514", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "851": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 851, - "name": "Experimental and modeling studies of desensitization of P2X3 receptors (Sokolova et al. 2006)", - "repository_type": "github", - "summary": "\"The function of ATP-activated P2X3 receptors involved in pain sensation is modulated by desensitization, a phenomenon poorly understood.\r\n\r\nThe present study used patch-clamp recording from cultured rat or mouse sensory neurons and kinetic\r\nmodeling to clarify the properties of P2X3 receptor desensitization.\r\n\r\n...\r\nDesensitization properties were well accounted for by a cyclic model in which\r\nreceptors could be desensitized from either open or closed\r\nstates.\r\n\r\nRecovery was assumed to be a multistate process with distinct kinetics dependent on the agonist-dependent dissociation rate from desensitized receptors.\r\n\r\n...\r\nBy using subthreshold concentrations of an HAD (high-affinity desensitization)-potent agonist, it might be possible\r\nto generate sustained inhibition of P2X3 receptors for controlling\r\nchronic pain.\"", - "tags": [ - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 775, - "tag": "Nociception" - }, - { - "id": 865, - "tag": "Pascal/Delphi" - }, - { - "id": 1322, - "tag": "ModelDB:117691" - } - ], - "timestamp_created": "2024-01-11 15:28:36.608634+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/117691", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "852": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 852, - "name": "Simulation system of spinal cord motor nuclei and assoc. nerves and muscles (Cisi and Kohn 2008)", - "repository_type": "github", - "summary": "A Web-based simulation system of the spinal\r\ncord circuitry responsible for muscle control is described.\r\nThe simulator employs two-compartment motoneuron\r\nmodels for S, FR and FF types, with synaptic inputs acting\r\nthrough conductance variations. \r\n\r\nFour motoneuron pools\r\nwith their associated interneurons are represented in the\r\nsimulator, with the possibility of inclusion of more than\r\n2,000 neurons and 2,000,000 synapses. \r\n\r\n... \r\nInputs to the motoneuron pool come from populations of\r\ninterneurons (Ia reciprocal inhibitory interneurons, Ib\r\ninterneurons, and Renshaw cells) and from stochastic point\r\nprocesses associated with descending tracts. \r\n\r\n...\r\nThe generation of the H-reflex\r\nby the Ia-motoneuron pool system and its modulation\r\nby spinal cord interneurons is included in the simulation\r\nsystem.\r\n", - "tags": [ - { - "id": 849, - "tag": "Java (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 1323, - "tag": "ModelDB:117810" - } - ], - "timestamp_created": "2024-01-11 15:28:37.062494+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/117810", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "853": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 853, - "name": "Spike Response Model simulator (Jolivet et al. 2004, 2006, 2008)", - "repository_type": "github", - "summary": "The Spike Response Model (SRM) optimized on the experimental data in the Single-Neuron modelling Competition ( www.incf.org/community/competitions ) for edition 2007 and edition 2008. The Spike Response Model is a simplified model of neuronal excitability where current linearly integrates to an artificial threshold. After the spike, the threshold is augmented and the voltage follows a voltage kernel that is the average voltage trace during and after a spike. The parameters were chosen to best fit the observed spike times with a method outlined in Jolivet et al. (2006).", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 1324, - "tag": "ModelDB:117966" - } - ], - "timestamp_created": "2024-01-11 15:28:37.751852+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/117966", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "854": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 854, - "name": "Roles of I(A) and morphology in AP prop. in CA1 pyramidal cell dendrites (Acker and White 2007)", - "repository_type": "github", - "summary": "\" ...Using conductance-based models of CA1 pyramidal cells, we show that underlying \u201ctraveling wave attractors\u201d control action potential propagation in the apical dendrites. \r\n\r\nBy computing these attractors, we dissect and quantify the effects of IA channels and dendritic morphology on bAP amplitudes. \r\n\r\nWe find that non-uniform activation properties of IA can lead to backpropagation failure similar to that observed experimentally in these cells. \r\n... \"", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1325, - "tag": "ModelDB:118014" - } - ], - "timestamp_created": "2024-01-11 15:28:38.317615+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/118014", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "855": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 855, - "name": "Reduction of nonlinear ODE systems possessing multiple scales (Clewley et al. 2005)", - "repository_type": "github", - "summary": "\" ... We introduce a combined numerical and analytical technique that aids the identification of structure in a class of systems of nonlinear ordinary differential\r\nequations (ODEs) that are commonly applied in dynamical models of physical processes.\r\n... \r\nThese methods have been incorporated into a new software tool named Dssrt, which we demonstrate\r\non a limit cycle of a synaptically driven Hodgkin\u2013Huxley neuron model.\"", - "tags": [ - { - "id": 804, - "tag": "Bifurcation" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 866, - "tag": "Multiscale" - }, - { - "id": 830, - "tag": "XPP (web link to model)" - }, - { - "id": 1326, - "tag": "ModelDB:118020" - } - ], - "timestamp_created": "2024-01-11 15:28:38.790234+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/118020", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "856": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 856, - "name": "A model of the femur-tibia control system in stick insects (Stein et al. 2008)", - "repository_type": "github", - "summary": "We studied the femur-tibia joint control system of the insect leg, and its switch between resistance reflex in posture control and \"active reaction\" in walking. The \"active reaction\" is basically a reversal of the resistance reflex. Both responses are elicited by the same sensory input and the same neuronal network (the femur-tibia network). \r\nThe femur-tibia network was modeled by fitting the responses of model neurons to those obtained in animals. Each implemented neuron has a physiological counterpart. The strengths of 16 interneuronal pathways that integrate sensory input were then assigned three different values and varied independently, generating a database of more than 43 million network variants. The uploaded version contains the model that best represented the resistance reflex. Please see the README for more help.\r\nWe demonstrate that the combinatorial code of interneuronal pathways determines motor output. A switch between different behaviors such as standing to walking can thus be achieved by altering the strengths of selected sensory integration pathways.\r\n", - "tags": [ - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 867, - "tag": "MadSim" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 1327, - "tag": "ModelDB:118092" - } - ], - "timestamp_created": "2024-01-11 15:28:39.311052+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/118092", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "857": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 857, - "name": "Ca3 pyramidal neuron: membrane response near rest (Hemond et al. 2009)", - "repository_type": "github", - "summary": "In this paper, the model was used to show how the temporal summation of excitatory inputs in CA3 pyramidal neurons was affected by the presence of Ih in the dendrites in a frequency- and distance-dependent fashion.", - "tags": [ - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 1328, - "tag": "ModelDB:118098" - } - ], - "timestamp_created": "2024-01-11 15:28:39.819130+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/118098", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "858": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 858, - "name": "Evaluation of stochastic diff. eq. approximation of ion channel gating models (Bruce 2009)", - "repository_type": "github", - "summary": "Fox and Lu derived an algorithm based on stochastic differential equations for approximating the kinetics of ion channel gating that is simpler and faster than \"exact\" algorithms for simulating Markov process models of channel gating.\r\n\r\nHowever, the approximation may not be sufficiently accurate to predict statistics of action potential generation in some cases.\r\n\r\nThe objective of this study was to develop a framework for analyzing the inaccuracies and determining their origin.\r\n\r\nSimulations of a patch of membrane with voltage-gated sodium and potassium channels were performed using an exact algorithm for the kinetics of channel gating and the approximate algorithm of Fox & Lu.\r\n...\r\nThe results indicate that: (i) the source of the inaccuracy is that the Fox & Lu algorithm does not adequately describe the combined behavior of the multiple activation particles in each sodium and potassium channel, and (ii) the accuracy does not improve with increasing numbers of channels.", - "tags": [ - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1329, - "tag": "ModelDB:118195" - } - ], - "timestamp_created": "2024-01-11 15:28:40.284510+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/118195", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "859": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 859, - "name": "Tag Trigger Consolidation (Clopath and Ziegler et al. 2008)", - "repository_type": "github", - "summary": "This model simulates different phases of LTP/D, i.e. the induction or early phase, the setting of synaptic tags, a trigger process for protein synthesis, and a slow transition leading to synaptic consolidation namely the late phase of synaptic plasticity. The model explains a large body of experimental data on synaptic tagging and capture, cross-tagging, and the late phases of LTP and LTD. Moreover, the model accounts for the dependence of LTP and LTD induction on voltage and presynaptic stimulation frequency.", - "tags": [ - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 772, - "tag": "Maintenance" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 1330, - "tag": "ModelDB:118199" - } - ], - "timestamp_created": "2024-01-11 15:28:40.745670+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/118199", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "860": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 860, - "name": "Multisensory integration in the superior colliculus: a neural network model (Ursino et al. 2009)", - "repository_type": "github", - "summary": "\" ... The model includes three distinct neural areas:\r\ntwo unimodal areas (auditory and visual) are devoted to a\r\ntopological representation of external stimuli, and communicate\r\nvia synaptic connections with a third downstream\r\narea (in the SC) responsible for multisensory integration.\r\n\r\nThe present simulations show that the model, with a single\r\nset of parameters, can mimic various responses to different\r\ncombinations of external stimuli including the inverse\r\neffectiveness, both in terms of multisensory enhancement\r\nand contrast, the existence of within- and cross-modality\r\nsuppression between spatially disparate stimuli, a reduction\r\nof network settling time in response to cross-modal stimuli\r\ncompared with individual stimuli.\r\n...\"", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 762, - "tag": "Audition" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 726, - "tag": "Vision" - }, - { - "id": 1331, - "tag": "ModelDB:118261" - } - ], - "timestamp_created": "2024-01-11 15:28:41.209225+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/118261", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "861": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 861, - "name": "Moose/PyMOOSE: interoperable scripting in Python for MOOSE (Ray and Bhalla 2008)", - "repository_type": "github", - "summary": "\" ... We report the integration of Python scripting with the Multi-scale Object Oriented Simulation Environment (MOOSE). MOOSE is a general-purpose simulation system for compartmental neuronal models and for models of signaling pathways based on chemical kinetics. We show how the Python-scripting version of MOOSE, PyMOOSE, combines the power of a compiled simulator with the versatility and ease of use of Python. ... \"", - "tags": [ - { - "id": 868, - "tag": "MOOSE/PyMOOSE (web link to method)" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 751, - "tag": "Signaling pathways" - }, - { - "id": 1332, - "tag": "ModelDB:118326" - } - ], - "timestamp_created": "2024-01-11 15:28:41.666388+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/118326", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "862": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 862, - "name": "Gap junction coupled network of striatal fast spiking interneurons (Hjorth et al. 2009)", - "repository_type": "github", - "summary": "Gap junctions between striatal FS neurons has very weak ability to synchronise spiking. Input uncorrelated between neighbouring neurons is shunted, while correlated input is not.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 861, - "tag": "I_K,Na" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 1333, - "tag": "ModelDB:118389" - } - ], - "timestamp_created": "2024-01-11 15:28:42.139770+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/118389", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "863": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 863, - "name": "Phase oscillator models for lamprey central pattern generators (Varkonyi et al. 2008)", - "repository_type": "github", - "summary": "In our paper, Varkonyi et al. 2008, we derive phase oscillator models for the lamprey central pattern generator from two biophysically based segmental models. We\r\nstudy intersegmental coordination and show how these models can provide stable intersegmental phase lags observed in real animals.\r\n", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 863, - "tag": "Parameter sensitivity" - }, - { - "id": 823, - "tag": "Phase Response Curves" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 1334, - "tag": "ModelDB:118392" - } - ], - "timestamp_created": "2024-01-11 15:28:42.616376+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/118392", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "864": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 864, - "name": "Odor supported place cell model and goal navigation in rodents (Kulvicius et al. 2008)", - "repository_type": "github", - "summary": "\" ...\r\nHere we model odor supported place cells by using a simple feed-forward network and analyze the impact of olfactory cues on place cell formation and spatial navigation. \r\n\r\nThe obtained place cells are used to solve a goal navigation task by a novel mechanism based on self-marking by odor patches combined with a Q-learning algorithm. \r\n\r\nWe also analyze the impact of place cell remapping on goal directed behavior when switching between two environments. \r\n...\"", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 799, - "tag": "Place cell/field" - }, - { - "id": 791, - "tag": "Rate-coding model neurons" - }, - { - "id": 809, - "tag": "Reinforcement Learning" - }, - { - "id": 800, - "tag": "Spatial Navigation" - }, - { - "id": 1335, - "tag": "ModelDB:118434" - } - ], - "timestamp_created": "2024-01-11 15:28:43.080251+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/118434", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "865": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 865, - "name": "Virtual Retina: biological retina simulator, with contrast gain control (Wohrer and Kornprobst 2009)", - "repository_type": "github", - "summary": "\"We propose a new retina simulation software, called Virtual Retina, which transforms a video into spike trains.\r\n\r\nOur goal is twofold: Allow large scale simulations (up to 100,000 neurons) in reasonable processing times and keep a strong biological plausibility, taking into account implementation constraints. \r\n\r\n...\r\nThis software will be an evolutionary tool for neuroscientists that need realistic large-scale input spike trains in subsequent treatments, and for educational purposes.\"", - "tags": [ - { - "id": 776, - "tag": "C or Cplusplus program (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 1336, - "tag": "ModelDB:118524" - } - ], - "timestamp_created": "2024-01-11 15:28:43.634387+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/118524", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "866": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 866, - "name": "Calyx of Held, short term plasticity (Yang Z et al. 2009)", - "repository_type": "github", - "summary": "This model investigates mechanisms contributing to short term plasticity at the calyx of Held, a giant glutamatergic synapse in the mammalian brainstem auditory system. It is a stochastic version of the model described in:\r\n \r\nHennig, M., Postlethwaite, M., Forsythe, I.D. and Graham, B.P. (2007). A biophysical model of short-term plasticity at the calyx of Held. \r\nNeurocomputing, 70:1626-1629.\r\n\r\nThis version introduces stochastic vesicle recycling and release. It has been used to investigate the information transmission\r\nproperties of this synapse, as detailed in:\r\n\r\nYang, Z., Hennig, M., Postlethwaite, M., Forsythe, I.D. and Graham, B.P. (2008). \r\nWide-band information transmission at the calyx of Held. Neural Computation, 21(4):991-1018.\r\n", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - }, - { - "id": 753, - "tag": "Vestibular" - }, - { - "id": 1337, - "tag": "ModelDB:118554" - } - ], - "timestamp_created": "2024-01-11 15:28:44.091127+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/118554", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "867": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 867, - "name": "Response properties of neocort. neurons to temporally modulated noisy inputs (Koendgen et al. 2008)", - "repository_type": "github", - "summary": "Neocortical neurons are classified by current\u2013frequency relationship. This is a static description and it may be inadequate to interpret neuronal responses to time-varying stimuli. \r\n\r\nTheoretical studies (Brunel et al., 2001; Fourcaud-Trocm\u00e9 et al. 2003; Fourcaud-Trocm\u00e9 and Brunel 2005; Naundorf et al. 2005) suggested that single-cell dynamical response properties are necessary to interpret ensemble responses to fast input transients. Further, it was shown that input-noise linearizes and boosts the response bandwidth, and that the interplay between the barrage of noisy synaptic currents and the spike-initiation mechanisms determine the dynamical properties of the firing rate. \r\n\r\nIn order to allow a reader to explore such simulations, we prepared a simple NEURON implementation of the experiments performed in K\u00f6ndgen et al., 2008 (see also Fourcaud-Trocm\u00e9 al. 2003; Fourcaud-Trocm\u00e9 and Brunel 2005).\r\n\r\nIn addition, we provide sample MATLAB routines for exploring the sandwich model proposed in K\u00f6ndgen et al., 2008, employing a simple frequdency-domain filtering.\r\n\r\nThe simulations and the MATLAB routines are based on the linear response properties of layer 5 pyramidal cells estimated by injecting a superposition of a small-amplitude sinusoidal wave and a background noise, as in K\u00f6ndgen et al., 2008.", - "tags": [ - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 791, - "tag": "Rate-coding model neurons" - }, - { - "id": 1338, - "tag": "ModelDB:118631" - } - ], - "timestamp_created": "2024-01-11 15:28:44.558785+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/118631", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "868": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 868, - "name": "Drosophila projection neuron electrotonic structure (Gouwens and Wilson 2009)", - "repository_type": "github", - "summary": "We address the issue of how electrical signals propagate in Drosophila neurons by modeling the electrotonic structure of the antennal lobe projection neurons innervating glomerulus DM1. The readme file contains instructions for running the model.", - "tags": [ - { - "id": 786, - "tag": "Electrotonus" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1339, - "tag": "ModelDB:118662" - } - ], - "timestamp_created": "2024-01-11 15:28:45.049987+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/118662", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "869": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 869, - "name": "Permeation and inactivation of CaV1.2 Ca2+ channels (Babich et al. 2007)", - "repository_type": "github", - "summary": "The authors present data and a kinetics model of the CaV1.2 channel supporting the idea that Ca2+ block of the pore generates the U-shaped inactivation curve.", - "tags": [ - { - "id": 869, - "tag": "CalC Calcium Calculator (web link to model)" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1340, - "tag": "ModelDB:118759" - } - ], - "timestamp_created": "2024-01-11 15:28:45.676498+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/118759", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "870": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 870, - "name": "Facilitation model based on bound Ca2+ (Matveev et al. 2006)", - "repository_type": "github", - "summary": "\"Facilitation is a transient stimulation-induced increase in synaptic response, a\r\nubiquitous form of short-term synaptic plasticity that can regulate\r\nsynaptic transmission on fast time scales. \r\n\r\nIn their pioneering work, Katz and Miledi and Rahamimoff demonstrated the dependence of\r\nfacilitation on presynaptic Ca2+ influx and proposed that facilitation\r\nresults from the accumulation of residual Ca2+ bound to vesicle\r\nrelease triggers. \r\n\r\nHowever, this bound Ca2+ hypothesis appears to contradict the evidence that \r\nfacilitation is reduced by exogenous Ca2+ buffers. \r\n\r\nThis conclusion led to a widely held view that facilitation\r\nmust depend solely on the accumulation of Ca2+ in free form. \r\n\r\nHere we consider a more realistic implementation of the bound Ca2+\r\nmechanism, taking into account spatial diffusion of Ca2+, and show\r\nthat a model with slow Ca2+ unbinding steps can retain sensitivity to\r\nfree residual Ca2+. \r\n...\"", - "tags": [ - { - "id": 869, - "tag": "CalC Calcium Calculator (web link to model)" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 723, - "tag": "Facilitation" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1341, - "tag": "ModelDB:118797" - } - ], - "timestamp_created": "2024-01-11 15:28:46.273904+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/118797", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "871": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 871, - "name": "Loss of phase-locking in non-weakly coupled inhib. networks of type-I neurons (Oh and Matveev 2009)", - "repository_type": "github", - "summary": "...\r\nHere we examine the loss of\r\nsynchrony caused by an increase in inhibitory coupling\r\nin networks of type-I Morris\u2013Lecar model oscillators,\r\nwhich is characterized by a period-doubling cascade\r\nand leads to mode-locked states with alternation in the\r\nfiring order of the two cells, as reported recently by\r\nMaran and Canavier (J Comput Nerosci, 2008) for a\r\nnetwork of Wang-Buzs\u00e1ki model neurons. \r\n\r\nAlthough\r\nalternating-order firing has been previously reported as\r\na near-synchronous state, we show that the stable phase\r\ndifference between the spikes of the two Morris\u2013Lecar\r\ncells can constitute as much as 70% of the unperturbed\r\noscillation period. \r\n\r\nFurther, we examine the generality\r\nof this phenomenon for a class of type-I oscillators that\r\nare close to their excitation thresholds, and provide\r\nan intuitive geometric description of such \u201cleap-frog\u201d\r\ndynamics. \r\n...\"", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 1342, - "tag": "ModelDB:118799" - } - ], - "timestamp_created": "2024-01-11 15:28:46.760530+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/118799", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "872": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 872, - "name": "Stochastic automata network Markov model descriptors of coupled Ca2+ channels (Nguyen et al. 2005)", - "repository_type": "github", - "summary": "\"... Here we present a formalism by which mathematical models for Ca2+-regulated Ca2+ release sites\r\nare derived from stochastic models of single-channel gating that include Ca2+ activation, Ca2+\r\ninactivation, or both. \r\n\r\nSuch models are stochastic automata networks (SANs) that involve a large\r\nnumber of functional transitions, that is, the transition probabilities of the infinitesimal generator\r\nmatrix of one of the automata (i.e., an individual channel) may depend on the local [Ca2+] and\r\nthus the state of the other channels. Simulation and analysis of the SAN descriptors representing\r\nhomogeneous clusters of intracellular Ca2+ channels show that (1) release site density can modify\r\nboth the steady-state open probability and stochastic excitability of Ca2+ release sites, (2) Ca2+\r\ninactivation is not a requirement for Ca2+ puffs or sparks, and (3) a single-channel model with a\r\nbell-shaped open probability curve does not lead to release site activity that is a biphasic function of\r\nrelease site density.\r\n...\"", - "tags": [ - { - "id": 869, - "tag": "CalC Calcium Calculator (web link to model)" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 748, - "tag": "Markov-type model" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1343, - "tag": "ModelDB:118894" - } - ], - "timestamp_created": "2024-01-11 15:28:47.236737+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/118894", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "873": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 873, - "name": "CA1 pyramidal neurons: effects of a Kv7.2 mutation (Miceli et al. 2009)", - "repository_type": "github", - "summary": "NEURON mod files from the paper:\r\nMiceli et al, Neutralization of a unique, negatively-charged residue in the voltage sensor \r\nof K(V)7.2 subunits in a sporadic case of benign familial neonatal seizures, Neurobiol Dis., in press (2009).\r\nIn this paper, the model revealed that the gating changes introduced by a mutation in K(v)7.2 \r\ngenes encoding for the neuronal KM current in a case of benign familial neonatal seizures,\r\nincreased cell firing frequency, thereby triggering the neuronal hyperexcitability which underlies the observed neonatal epileptic condition.\r\n", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1344, - "tag": "ModelDB:118986" - } - ], - "timestamp_created": "2024-01-11 15:28:47.737646+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/118986", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "874": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 874, - "name": "Facilitation through buffer saturation (Matveev et al. 2004)", - "repository_type": "github", - "summary": "\"... Using computer simulations, we\r\nstudy the magnitude of synaptic facilitation (SF) that can be achieved by a buffer saturation mechanism (BSM), and explore its dependence on the\r\nendogenous buffering properties.\r\n...\"", - "tags": [ - { - "id": 869, - "tag": "CalC Calcium Calculator (web link to model)" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 723, - "tag": "Facilitation" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 1345, - "tag": "ModelDB:119153" - } - ], - "timestamp_created": "2024-01-11 15:28:48.243552+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/119153", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "875": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 875, - "name": "STDP promotes synchrony of inhibitory networks in the presence of heterogeneity (Talathi et al 2008)", - "repository_type": "github", - "summary": "\"Recently Haas et al. (J Neurophysiol 96:\r\n3305\u20133313, 2006), observed a novel form of spike timing\r\ndependent plasticity (iSTDP) in GABAergic synaptic\r\ncouplings in layer II of the entorhinal cortex. Depending\r\non the relative timings of the presynaptic input at\r\ntime tpre and the postsynaptic excitation at time tpost,\r\nthe synapse is strengthened (delta_t = t(post) - t(pre) > 0) or\r\nweakened (delta_t < 0). The temporal dynamic range of\r\nthe observed STDP rule was found to lie in the higher\r\ngamma frequency band (> or = 40 Hz), a frequency range\r\nimportant for several vital neuronal tasks. In this paper\r\nwe study the function of this novel form of iSTDP in\r\nthe synchronization of the inhibitory neuronal network.\r\nIn particular we consider a network of two unidirectionally\r\ncoupled interneurons (UCI) and two mutually\r\ncoupled interneurons (MCI), in the presence of\r\nheterogeneity in the intrinsic firing rates of each coupled\r\nneuron. ...\"", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 802, - "tag": "STDP" - }, - { - "id": 1346, - "tag": "ModelDB:119159" - } - ], - "timestamp_created": "2024-01-11 15:28:48.708368+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/119159", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "876": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 876, - "name": "New and corrected simulations of synaptic facilitation (Matveev et al. 2002)", - "repository_type": "github", - "summary": "\"A three-dimensional presynaptic calcium diffusion model developed to account for characteristics of transmitter release\r\nwas modified to provide for binding of calcium to a receptor and subsequent triggering of exocytosis. \r\n\r\nWhen low affinity (20 FLM) and\r\nrapid kinetics were assumed for the calcium receptor triggering exocytosis, and stimulus parameters were selected to match those\r\nof experiments, the simulations predicted a virtual invariance of the time course of transmitter release to paired stimulation,\r\nstimulation with pulses of different amplitude, and stimulation in different calcium solutions. \r\n...\"", - "tags": [ - { - "id": 869, - "tag": "CalC Calcium Calculator (web link to model)" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 723, - "tag": "Facilitation" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1347, - "tag": "ModelDB:119214" - } - ], - "timestamp_created": "2024-01-11 15:28:49.262124+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/119214", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "877": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 877, - "name": "Modelling reduced excitability in aged CA1 neurons as a Ca-dependent process (Markaki et al. 2005)", - "repository_type": "github", - "summary": "\"We use a multi-compartmental model of a CA1 pyramidal cell to study changes in hippocampal excitability that result from aging-induced alterations in calcium-dependent membrane mechanisms.\r\n\r\nThe model incorporates N- and L-type calcium channels which are respectively coupled to fast and slow afterhyperpolarization potassium channels.\r\n\r\nModel parameters are calibrated using physiological data.\r\n\r\nComputer simulations reproduce the decreased excitability of aged CA1 cells, which results from increased internal calcium accumulation, subsequently larger postburst slow afterhyperpolarization, and enhanced spike frequency adaptation.\r\n\r\nWe find that aging-induced alterations in CA1 excitability can be modelled with simple coupling mechanisms that selectively link specific types of calcium channels to specific calcium-dependent potassium channels.\"", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 827, - "tag": "Aging/Alzheimer`s" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 844, - "tag": "I R" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1348, - "tag": "ModelDB:119266" - } - ], - "timestamp_created": "2024-01-11 15:28:49.790053+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/119266", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "878": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 878, - "name": "Computational neuropharmacology of CA1 pyramidal neuron (Ferrante et al. 2008)", - "repository_type": "github", - "summary": "In this paper, the model was used to show how neuroactive drugs targeting different neuronal mechanisms affect the signal integration in CA1 pyramidal neuron. Ferrante M, Blackwell KT, Migliore M, Ascoli GA (2008)", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 827, - "tag": "Aging/Alzheimer`s" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 787, - "tag": "Pathophysiology" - }, - { - "id": 845, - "tag": "Schizophrenia" - }, - { - "id": 777, - "tag": "Spike Frequency Adaptation" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 1349, - "tag": "ModelDB:119283" - } - ], - "timestamp_created": "2024-01-11 15:28:50.320240+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/119283", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "879": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 879, - "name": "Transmitter release and Ca diffusion models (Yamada and Zucker 1992)", - "repository_type": "github", - "summary": "\"A three-dimensional presynaptic calcium diffusion model developed to account for characteristics of transmitter release was modified to provide for binding of calcium to a receptor and subsequent triggering of exocytosis. ...\"", - "tags": [ - { - "id": 869, - "tag": "CalC Calcium Calculator (web link to model)" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 723, - "tag": "Facilitation" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1350, - "tag": "ModelDB:120115" - } - ], - "timestamp_created": "2024-01-11 15:28:50.797766+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/120115", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "880": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 880, - "name": "TTX-R Na+ current effect on cell response (Herzog et al 2001) (MATLAB)", - "repository_type": "github", - "summary": "\"Small dorsal root ganglion (DRG) neurons, which include nociceptors, express multiple voltage-gated sodium currents. In addition to a classical fast inactivating tetrodotoxin-sensitive (TTX-S) sodium current, many of these cells express a TTX-resistant (TTX-R) sodium current that activates near -70 mV and is persistent at negative potentials. To investigate the possible contributions of this TTX-R persistent (TTX-RP) current to neuronal excitability, we carried out computer simulations using the Neuron program with TTX-S and -RP currents, fit by the Hodgkin-Huxley model, that closely matched the currents recorded from small DRG neurons. ...\" See paper for more and details.", - "tags": [ - { - "id": 576, - "tag": "I K" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 775, - "tag": "Nociception" - }, - { - "id": 1351, - "tag": "ModelDB:120117" - } - ], - "timestamp_created": "2024-01-11 15:28:51.278854+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/120117", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "881": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 881, - "name": "A finite volume method for stochastic integrate-and-fire models (Marpeau et al. 2009)", - "repository_type": "github", - "summary": "\"The stochastic integrate and fire neuron is\r\none of the most commonly used stochastic models\r\nin neuroscience.\r\n\r\nAlthough some cases are analytically\r\ntractable, a full analysis typically calls for numerical\r\nsimulations.\r\n\r\nWe present a fast and accurate finite volume\r\nmethod to approximate the solution of the associated\r\nFokker-Planck equation. ...\"", - "tags": [ - { - "id": 870, - "tag": "FORTRAN (web link to a model)" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 1352, - "tag": "ModelDB:120137" - } - ], - "timestamp_created": "2024-01-11 15:28:51.746598+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/120137", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "882": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 882, - "name": "A kinetic model unifying presynaptic short-term facilitation and depression (Lee et al. 2009)", - "repository_type": "github", - "summary": "\"...\r\n\r\nHere, we propose a unified theory of synaptic short-term plasticity based on realistic yet tractable and testable model descriptions of the underlying intracellular biochemical processes. \r\n\r\nAnalysis of the model equations leads to a closed-form solution of the resonance frequency, a function of several critical biophysical parameters, as the single key indicator of the propensity for synaptic facilitation or depression under repetitive stimuli. \r\n\r\nThis integrative model is supported by a broad range of transient and frequency response experimental data including those from facilitating, depressing or mixed-mode synapses. \r\n... the model provides the reasons behind the switching behavior between facilitation and depression observed in experiments. ...\"\r\n", - "tags": [ - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 722, - "tag": "Depression" - }, - { - "id": 723, - "tag": "Facilitation" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 824, - "tag": "Simulink" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 1353, - "tag": "ModelDB:120184" - } - ], - "timestamp_created": "2024-01-11 15:28:52.238886+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/120184", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "883": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 883, - "name": "Multistability of clustered states in a globally inhibitory network (Chandrasekaran et al. 2009)", - "repository_type": "github", - "summary": "\"We study a network of m identical excitatory cells projecting excitatory synaptic connections onto a single inhibitory interneuron, which is reciprocally coupled to all excitatory cells through inhibitory synapses possessing short-term synaptic depression.\r\n\r\nWe find that such a network with global inhibition possesses multiple stable activity patterns with distinct periods, characterized by the clustering of the excitatory cells into synchronized sub-populations.\r\n\r\nWe prove the existence and stability of n-cluster solutions in a m-cell network.\r\n\r\n... Implications for temporal coding and memory storage are discussed.\"", - "tags": [ - { - "id": 718, - "tag": "Attractor Neural Network" - }, - { - "id": 722, - "tag": "Depression" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 1354, - "tag": "ModelDB:120227" - } - ], - "timestamp_created": "2024-01-11 15:28:53.043448+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/120227", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "884": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 884, - "name": "Contribution of ATP-sensitive potassium channels in the neuronal network (Huang et al. 2009)", - "repository_type": "github", - "summary": "Epileptic seizures in diabetic hyperglycemia (DH) are not uncommon. \r\n\r\nThis study aimed to determine the acute behavioral, pathological, and electrophysiological effects of status epilepticus (SE) on diabetic animals. \r\n\r\n...\r\n\r\nWe also used a simulation model to evaluate intracellular adenosine triphosphate (ATP) and neuroexcitability.\r\n\r\n...\r\nIn the simulation, increased intracellular ATP concentration promoted action potential firing.\r\n\r\nThis finding that rats with DH had more brain damage after SE than rats without diabetes suggests the importance of intensively treating hyperglycemia and seizures in diabetic patients with epilepsy.", - "tags": [ - { - "id": 795, - "tag": "ATP-senstive potassium current" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1355, - "tag": "ModelDB:120243" - } - ], - "timestamp_created": "2024-01-11 15:28:53.589158+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/120243", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "885": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 885, - "name": "Action potential of adult rat ventricle (Wang et al. 2008)", - "repository_type": "github", - "summary": "\"Aconitine (ACO), a highly toxic diterpenoid alkaloid, is recognized to have effects \r\non cardiac voltage-gated Na(+) channels. However, it remains unknown whether it has \r\nany effects on K(+) currents. The effects of ACO on ion currents in differentiated \r\nclonal cardiac (H9c2) cells and in cultured neonatal rat ventricular myocytes were \r\ninvestigated in this study. ...\" The rat action potential in this simulation was played back into the cell for experiments reported in this paper.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 759, - "tag": "XPPAUT" - }, - { - "id": 1356, - "tag": "ModelDB:120246" - } - ], - "timestamp_created": "2024-01-11 15:28:54.081488+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/120246", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "886": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 886, - "name": "A nicotinic acetylcholine receptor kinetic model (Edelstein et al. 1996)", - "repository_type": "github", - "summary": "Nicotinic acetylcholine receptors are transmembrane\r\noligomeric proteins that mediate interconversions\r\nbetween open and closed channel states under the\r\ncontrol of neurotransmitters. \r\n\r\n..\r\n\r\nIn order to represent the functional properties of such\r\nreceptors, we have developed a kinetic model that links\r\nconformational interconversion rates to agonist binding\r\nand extends the general principles of the Monod-\r\nWyman-Changeux model of allosteric transitions. \r\n\r\n...\r\nApplication of the model to the peripheral nicotinic acetylcholine receptor\r\n(nAChR) accounts for the main properties of ligand-gating,\r\nincluding single-channel events, and several new\r\nrelationships are predicted.\r\n...\r\nIn terms of future developments, the analysis presented here provides\r\na physical basis for constructing more biologically realistic\r\nmodels of synaptic modulation that may be applied to\r\nartificial neural networks.\r\n", - "tags": [ - { - "id": 871, - "tag": "BioPAX (web link to model)" - }, - { - "id": 872, - "tag": "CellML (web link to model)" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 828, - "tag": "SBML (web link to model)" - }, - { - "id": 854, - "tag": "Scilab (web link to model)" - }, - { - "id": 782, - "tag": "Virtual Cell (web link to model)" - }, - { - "id": 829, - "tag": "XML (web link to model)" - }, - { - "id": 830, - "tag": "XPP (web link to model)" - }, - { - "id": 1357, - "tag": "ModelDB:120320" - } - ], - "timestamp_created": "2024-01-11 15:28:54.552335+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/120320", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "887": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 887, - "name": "Nodose sensory neuron (Schild et al. 1994, Schild and Kunze 1997)", - "repository_type": "github", - "summary": "This is a simulink implementation of the model described in Schild et al. 1994, and Schild and Kunze 1997 papers on Nodose sensory neurons. These papers describe the sensitivity these models have to their parameters and the match of the models to experimental data.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 738, - "tag": "Na/Ca exchanger" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 863, - "tag": "Parameter sensitivity" - }, - { - "id": 1358, - "tag": "ModelDB:120521" - } - ], - "timestamp_created": "2024-01-11 15:28:55.125378+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/120521", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "888": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 888, - "name": "Sodium potassium ATPase pump (Chapman et al. 1983)", - "repository_type": "github", - "summary": "The electrochemical properties of a widely accepted six-step reaction scheme for the Na,K-ATPase have been studied by computer simulation.", - "tags": [ - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 740, - "tag": "Na/K pump" - }, - { - "id": 741, - "tag": "Sodium pump" - }, - { - "id": 1359, - "tag": "ModelDB:120692" - } - ], - "timestamp_created": "2024-01-11 15:28:55.617271+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/120692", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "889": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 889, - "name": "Generating oscillatory bursts from a network of regular spiking neurons (Shao et al. 2009)", - "repository_type": "github", - "summary": "Avian nucleus isthmi pars parvocellularis (Ipc) neurons are reciprocally connected with the tectal layer 10 (L10) neurons and respond with oscillatory bursts to visual stimulation. To elucidate mechanisms of oscillatory bursting in this network of regularly spiking neurons, we investigated an experimentally constrained model of coupled leaky integrate-and-fire neurons with spike-rate adaptation. The model reproduces the observed Ipc oscillatory bursting in response to simulated visual stimulation.", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 726, - "tag": "Vision" - }, - { - "id": 1360, - "tag": "ModelDB:120783" - } - ], - "timestamp_created": "2024-01-11 15:28:56.149647+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/120783", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "890": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 890, - "name": "Membrane potential changes in dendritic spines during APs and synaptic input (Palmer & Stuart 2009)", - "repository_type": "github", - "summary": "\" ...\r\nFinally, we used simulations of our experimental observations in\r\nmorphologically realistic models to estimate spine neck resistance.\r\n\r\nThese simulations indicated that spine neck resistance ranges up\r\nto ~500 M Ohm.\r\n\r\nSpine neck resistances of this magnitude reduce somatic EPSPs by ~15%,\r\nindicating that the spine neck is unlikely to act as a physical device\r\nto significantly modify synaptic strength.\"", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 1361, - "tag": "ModelDB:120798" - } - ], - "timestamp_created": "2024-01-11 15:28:56.665455+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/120798", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "891": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 891, - "name": "A model of beta-adrenergic modulation of IKs in the guinea-pig ventricle (Severi et al. 2009)", - "repository_type": "github", - "summary": "Detailed understanding of IKs gating complexity may provide clues on the mechanisms of cardiac repolarization instability and the resulting arrhythmias. We developed and tested a kinetic Markov model to interpret physiologically relevant IKs properties, including pause-dependency and modulation by beta-adrenergic receptors (beta-AR). The model was developed from the Silva & Rudy formulation. Parameters were optimized on control and ISO experimental data, respectively.", - "tags": [ - { - "id": 783, - "tag": "I_Ks" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 748, - "tag": "Markov-type model" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 824, - "tag": "Simulink" - }, - { - "id": 1362, - "tag": "ModelDB:120835" - } - ], - "timestamp_created": "2024-01-11 15:28:57.139314+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/120835", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "892": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 892, - "name": "Mechanisms of very fast oscillations in axon networks coupled by gap junctions (Munro, Borgers 2010)", - "repository_type": "github", - "summary": "Axons connected by gap junctions can produce very fast oscillations (VFOs, > 80 Hz) when stimulated randomly at a low rate. The models here explore the mechanisms of VFOs that can be seen in an axonal plexus, (Munro & Borgers, 2009): a large network model of an axonal plexus, small network models of axons connected by gap junctions, and an implementation of the model underlying figure 12 in Traub et al. (1999) .\r\n\r\nThe large network model consists of 3,072 5-compartment axons connected in a random network. The 5-compartment axons are the 5 axonal compartments from the CA3 pyramidal cell model in Traub et al. (1994) with a fixed somatic voltage. The random network has the same parameters as the random network in Traub et al. (1999), and axons are stimulated randomly via a Poisson process with a rate of 2/s/axon. \r\n\r\nThe small network models simulate waves propagating through small networks of axons connected by gap junctions to study how local connectivity affects the refractory period.\r\n", - "tags": [ - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 745, - "tag": "Conduction failure" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 1363, - "tag": "ModelDB:120907" - } - ], - "timestamp_created": "2024-01-11 15:28:57.622158+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/120907", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "893": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 893, - "name": "Electrically-coupled Retzius neurons (Vazquez et al. 2009)", - "repository_type": "github", - "summary": "\"Dendritic electrical coupling increases the number of effective synaptic inputs\r\nonto neurons by allowing the direct spread of synaptic potentials from one\r\nneuron to another. \r\n\r\nHere we studied the summation of excitatory postsynaptic potentials (EPSPs) produced \r\nlocally and arriving from the coupled neuron (transjunctional) in pairs of \r\nelectrically-coupled Retzius neurons of the leech.\r\n\r\nWe combined paired recordings of EPSPs, the production of artificial EPSPs\r\n(APSPs) in neuron pairs with different coupling coefficients and simulations of\r\nEPSPs produced in the coupled dendrites. \r\n...\"", - "tags": [ - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 1364, - "tag": "ModelDB:120910" - } - ], - "timestamp_created": "2024-01-11 15:28:58.183903+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/120910", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "894": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 894, - "name": "Effects of KIR current inactivation in NAc Medium Spiny Neurons (Steephen and Manchanda 2009)", - "repository_type": "github", - "summary": "\"Inward rectifying potassium (KIR) currents in medium spiny (MS) neurons of nucleus accumbens inactivate significantly in ~40% of the neurons but not in the rest, which may lead to differences in input processing by these two groups. \r\n\r\nUsing a 189-compartment computational model of the MS neuron, we investigate the influence of this property using injected current as well as spatiotemporally distributed synaptic inputs. \r\n\r\nOur study demonstrates that KIR current inactivation facilitates depolarization, firing frequency and firing onset in these neurons. ...\"", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 754, - "tag": "Delay" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 731, - "tag": "I CAN" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 842, - "tag": "I Krp" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 844, - "tag": "I R" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 742, - "tag": "I p,q" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 1365, - "tag": "ModelDB:121060" - } - ], - "timestamp_created": "2024-01-11 15:28:58.725169+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/121060", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "895": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 895, - "name": "Submyelin Potassium accumulation in Subthalamic neuron (STN) axons (Bellinger et al. 2008)", - "repository_type": "github", - "summary": "\"To better understand the direct effects of DBS (Deep brain stimulation) on central neurons, a computational model of a myelinated axon has been constructed which includes the effects of K+ accumulation within the peri-axonal space.\r\nUsing best estimates of anatomic and electrogenic model parameters for in vivo STN axons, the model predicts a functional block along the axon due to K+ accumulation in the submyelin space.\r\n...\r\nThese results suggest that therapeutic DBS of the STN likely results in a functional block for many STN axons, although a subset of STN axons may also be activated at the stimulating frequency.\r\n\"", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 831, - "tag": "Deep brain stimulation" - }, - { - "id": 873, - "tag": "Depolarization block" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 783, - "tag": "I_Ks" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 740, - "tag": "Na/K pump" - }, - { - "id": 741, - "tag": "Sodium pump" - }, - { - "id": 1366, - "tag": "ModelDB:121253" - } - ], - "timestamp_created": "2024-01-11 15:28:59.237738+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/121253", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "896": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 896, - "name": "Small world networks of Type I and Type II Excitable Neurons (Bogaard et al. 2009)", - "repository_type": "github", - "summary": "Implemented with NEURON 5.9, four model neurons with varying excitability properties affect the spatiotemporal patterning of small world networks of homogeneous and heterogeneous cell population.", - "tags": [ - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 1367, - "tag": "ModelDB:121259" - } - ], - "timestamp_created": "2024-01-11 15:28:59.832010+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/121259", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "897": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 897, - "name": "Thalamic neuron: Modeling rhythmic neuronal activity (Meuth et al. 2005)", - "repository_type": "github", - "summary": "The authors use an in vitro cell model of a single acutely isolated thalamic neuron in the NEURON simulation environment to address and discuss questions in an undergraduate course. Topics covered include passive electrical properties, composition of action potentials, trains of action potentials, multicompartment modeling, and research topics. The paper includes detailed instructions on how to run the simulations in the appendix.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 732, - "tag": "Tutorial/Teaching" - }, - { - "id": 1368, - "tag": "ModelDB:121600" - } - ], - "timestamp_created": "2024-01-11 15:29:00.323418+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/121600", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "898": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 898, - "name": "Software for teaching neurophysiology of neuronal circuits (Grisham et al. 2008)", - "repository_type": "github", - "summary": "\"To circumvent the many problems in teaching neurophysiology as a \u201cwet lab,\u201d we developed SWIMMY, a virtual fish that swims by moving its virtual tail by means of a virtual neural circuit.\r\n...\r\nUsing SWIMMY, students (1) review the basics of neurophysiology, (2) identify the neurons in the circuit, (3) ascertain the neurons\u2019 synaptic interconnections, (4) discover which cells generate the motor pattern of swimming, (5) discover how the rhythm is generated, and finally (6) use an animation that corresponds to the activity of the motoneurons to discover the behavioral effects produced by various lesions and explain them in terms of their neural underpinnings.\r\n...\"", - "tags": [ - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 743, - "tag": "NEURON (web link to model)" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 732, - "tag": "Tutorial/Teaching" - }, - { - "id": 1369, - "tag": "ModelDB:121628" - } - ], - "timestamp_created": "2024-01-11 15:29:00.797706+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/121628", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "899": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 899, - "name": "Model of SK current`s influence on precision in Globus Pallidus Neurons (Deister et al. 2009)", - "repository_type": "github", - "summary": "\" ... In numerical simulations, the availability of both Na+ and A-type K+ channels during autonomous firing were reduced when SK channels were removed, and a nearly equal reduction in Na+ and K+ subthreshold-activated ion channel availability produced a large decrease in the neuron's slope conductance near threshold. \r\n\r\nThis change made the neuron more sensitive to intrinsically generated noise.\r\n\r\nIn vivo, this change would also enhance the sensitivity of GP (Globus Pallidus) neurons to small synaptic inputs.\"\r\n", - "tags": [ - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 874, - "tag": "Noise Sensitivity" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 1370, - "tag": "ModelDB:122329" - } - ], - "timestamp_created": "2024-01-11 15:29:01.289126+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/122329", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "900": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 900, - "name": "Investigation of different targets in deep brain stimulation for Parkinson`s (Pirini et al. 2009)", - "repository_type": "github", - "summary": "\"We investigated by a computational model of the basal ganglia the different network effects of deep brain stimulation (DBS) for Parkinson\u2019s disease (PD) in different target sites in the subthalamic nucleus (STN), the globus pallidus pars interna (GPi), and the globus pallidus pars externa (GPe). \r\n\r\nA cellular-based model of the basal ganglia system (BGS), based on the model proposed by Rubin and Terman (J Comput Neurosci 16:211\u2013235, 2004), was developed.\r\n...\r\nOur results suggest that DBS in the STN could functionally restore the TC relay activity, while DBS in the GPe and in the GPi could functionally over-activate and inhibit it, respectively.\r\n\r\nOur results are consistent with the experimental and the clinical evidences on the network effects of DBS.\"\r\n", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 831, - "tag": "Deep brain stimulation" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 1371, - "tag": "ModelDB:122369" - } - ], - "timestamp_created": "2024-01-11 15:29:01.774187+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/122369", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "901": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 901, - "name": "Tight junction model of CNS myelinated axons (Devaux and Gow 2008)", - "repository_type": "github", - "summary": "Two models are included:\r\n1) a myelinated axon is represented by an equivalent circuit with a double cable design but includes a tight junction in parallel with the myelin membrane RC circuit (called double cable model, DCM).\r\n\r\n2) a myelinated axon is represented by an equivalent circuit with a double cable design but includes a tight junction in series with the myelin RC circuit (called tight junction model, TJM).\r\n\r\nThese models have been used to simulate data from compound action potentials measured in mouse optic nerve from Claudin 11-null mice in Fig. 6 of:\r\nDevaux, J.J. & Gow, A. (2008) Tight Junctions Potentiate The Insulative Properties Of Small CNS Myelinated Axons. J Cell Biol 183, 909-921.\r\n", - "tags": [ - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1372, - "tag": "ModelDB:122442" - } - ], - "timestamp_created": "2024-01-11 15:29:02.486440+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/122442", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "902": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 902, - "name": "CONFIGR: a vision-based model for long-range figure completion (Carpenter et al. 2007)", - "repository_type": "github", - "summary": "\"CONFIGR (CONtour FIgure GRound) is a computational model based on\r\nprinciples of\r\nbiological vision that completes sparse and noisy image figures.\r\n\r\nWithin an integrated\r\nvision/recognition system, CONFIGR posits an initial recognition\r\nstage which identifies figure\r\npixels from spatially local input information.\r\n\r\nThe resulting, and typically incomplete, figure is\r\nfed back to the \u201cearly vision\u201d stage for long-range completion via\r\nfilling-in.\r\n\r\nThe reconstructed\r\nimage is then re-presented to the recognition system for global\r\nfunctions such as object\r\nrecognition.\r\n\r\n...\r\nMulti-scale simulations illustrate the\r\nvision/recognition system. \r\n\r\n...\"\r\n", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 719, - "tag": "Pattern Recognition" - }, - { - "id": 1373, - "tag": "ModelDB:123086" - } - ], - "timestamp_created": "2024-01-11 15:29:02.965887+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/123086", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "903": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 903, - "name": "Effect of voltage sensitive fluorescent proteins on neuronal excitability (Akemann et al. 2009)", - "repository_type": "github", - "summary": "\"Fluorescent protein voltage sensors are recombinant proteins that are designed as genetically encoded cellular\r\nprobes of membrane potential using mechanisms of voltage-dependent modulation of fluorescence. \r\n\r\nSeveral such proteins,\r\nincluding VSFP2.3 and VSFP3.1, were recently reported with reliable function in mammalian cells. \r\n\r\n...\r\n\r\nExpression of these proteins in cell membranes is accompanied by additional dynamic membrane capacitance, ...\r\n\r\nWe used recordings of\r\nsensing currents and fluorescence responses of VSFP2.3 and of VSFP3.1 to derive kinetic models of the voltage-dependent\r\nsignaling of these proteins. \r\n\r\nUsing computational neuron simulations, we quantitatively investigated the perturbing effects of\r\nsensing capacitance on the input/output relationship in two central neuron models, a cerebellar Purkinje and a layer 5 pyramidal\r\nneuron. \r\n... \". The Purkinje cell model is included in ModelDB.", - "tags": [ - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1374, - "tag": "ModelDB:123453" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:29:35.908495+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/123453", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "904": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 904, - "name": "Hodgkin-Huxley models of different classes of cortical neurons (Pospischil et al. 2008)", - "repository_type": "github", - "summary": "\"We review here the development of Hodgkin-\r\nHuxley (HH) type models of cerebral cortex and thalamic\r\nneurons for network simulations.\r\n\r\nThe intrinsic electrophysiological\r\nproperties of cortical neurons were analyzed from\r\nseveral preparations, and we selected the four most prominent\r\nelectrophysiological classes of neurons.\r\n\r\nThese four classes\r\nare 'fast spiking', 'regular spiking', 'intrinsically bursting'\r\nand 'low-threshold spike' cells. For each class, we fit 'minimal'\r\nHH type models to experimental data.\r\n...\"", - "tags": [ - { - "id": 576, - "tag": "I K" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1375, - "tag": "ModelDB:123623" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 569, - "tag": "Simplified Models" - } - ], - "timestamp_created": "2024-01-12 09:29:36.859223+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/123623", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "905": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 905, - "name": "Encoding and retrieval in a model of the hippocampal CA1 microcircuit (Cutsuridis et al. 2009)", - "repository_type": "github", - "summary": "This NEURON code implements a small network model (100 pyramidal cells\r\nand 4 types of inhibitory interneuron) of storage and recall of patterns\r\nin the CA1 region of the mammalian hippocampus. Patterns of PC activity\r\nare stored either by a predefined weight matrix generated by Hebbian learning,\r\nor by STDP at CA3 Schaffer collateral AMPA synapses.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 855, - "tag": "Connectivity matrix" - }, - { - "id": 825, - "tag": "Learning" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1376, - "tag": "ModelDB:123815" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 719, - "tag": "Pattern Recognition" - }, - { - "id": 802, - "tag": "STDP" - }, - { - "id": 1377, - "tag": "Storage/recall" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - } - ], - "timestamp_created": "2024-01-12 09:29:37.477484+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/123815", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "906": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 906, - "name": "Na channel mutations in the dentate gyrus (Thomas et al. 2009)", - "repository_type": "github", - "summary": "These are source files to generate the data in Figure 6 from\r\n\"Mossy fiber sprouting interacts with sodium channel\r\nmutations to increase dentate gyrus excitability\" Thomas EA, Reid CA, Petrou S,\r\nEpilepsia (2009)", - "tags": [ - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1378, - "tag": "ModelDB:123848" - }, - { - "id": 1379, - "tag": "parplex" - } - ], - "timestamp_created": "2024-01-12 09:29:38.082016+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/123848", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "907": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 907, - "name": "Action Potential initiation and backpropagation in Neocortical L5 Pyramidal Neuron (Hu et al. 2009)", - "repository_type": "github", - "summary": "\"...Previous computational studies have yielded conflicting conclusions\r\nabout the role of Na+ channel density and biophysical properties in\r\naction potential initiation as a result of inconsistent estimates of\r\nchannel density. Our modeling studies integrated the immunostaining\r\nand electrophysiological results and showed that the lowest\r\nthreshold for action potential initiation at the distal AIS was largely\r\ndetermined by the density of low-threshold Nav1.6 channels ... Distinct from the function of Nav1.6 channel, the Nav1.2 channel\r\nmay control action potential backpropagation because of its high\r\ndensity at the proximal AIS and high threshold. ... In conclusion, distal AIS accumulation of Nav1.6 channels determines\r\nthe low threshold for action potential initiation; whereas\r\nproximal AIS accumulation of Nav1.2 channels sets the threshold for\r\nthe generation of somatodendritic potentials and ensures action\r\npotential backpropagation to the soma and dendrites. Thus, Nav1.6\r\nand Nav1.2 channels serve distinct functions in action potential\r\ninitiation and backpropagation.\"", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1380, - "tag": "ModelDB:123897" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:29:38.679898+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/123897", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "908": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 908, - "name": "Complex CA1-neuron to study AP initiation (Wimmer et al. 2010)", - "repository_type": "github", - "summary": "Complex model of a pyramidal CA1-neuron, adapted from Royeck, M., et al. Role of axonal NaV1.6 sodium channels in action potential\r\ninitiation of CA1 pyramidal neurons. Journal of neurophysiology 100, 2361-2380\r\n(2008).\r\nIt contains a biophysically realistic morphology comprising 265 compartments (829 segments) and 15 different distributed Ca2+- and/or voltage-dependent conductances.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 742, - "tag": "I p,q" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1381, - "tag": "ModelDB:123927" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:29:39.339050+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/123927", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "909": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 909, - "name": "Oscillations, phase-of-firing coding and STDP: an efficient learning scheme (Masquelier et al. 2009)", - "repository_type": "github", - "summary": "The model demonstrates how a common oscillatory drive for a group of neurons formats and reliabilizes their spike times - through an activation-to-phase conversion - so that repeating activation patterns can be easily detected and learned by a downstream neuron equipped with STDP, and then recognized in just one oscillation cycle.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 645, - "tag": "Brian" - }, - { - "id": 595, - "tag": "Coincidence Detection" - }, - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1382, - "tag": "ModelDB:123928" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 719, - "tag": "Pattern Recognition" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 802, - "tag": "STDP" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 803, - "tag": "Unsupervised Learning" - } - ], - "timestamp_created": "2024-01-12 09:29:39.979783+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/123928", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "910": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 910, - "name": "Oscillation and coding in a proposed NN model of insect olfaction (Horcholle-Bossavit et al. 2007)", - "repository_type": "github", - "summary": "\"For the analysis of coding mechanisms in the insect olfactory system, a fully connected network of synchronously updated\r\nMcCulloch and Pitts neurons (MC-P type) was (previously) developed. ... Considering the update time as an intrinsic clock, this \u201cDynamic\r\nNeural Filter\u201d (DNF), which maps regions of input space into spatio-temporal sequences of neuronal activity, is able to produce\r\nexact binary codes extracted from the synchronized activities recorded at the level of projection neurons (PN) in the locust antennal\r\nlobe (AL) in response to different odors\r\n...\r\nWe find synaptic matrices which lead to both the emergence of robust oscillations and spatio-temporal patterns, using a\r\nformal criterion, based on a Normalized Euclidian Distance (NED), in order to measure the use of the temporal dimension as a\r\ncoding dimension by the DNF. Similarly to biological PN, the activity of excitatory neurons in the model can be both phase-locked\r\nto different cycles of oscillations which (is reminiscent of the) local field potential (LFP), and nevertheless exhibit dynamic behavior complex\r\nenough to be the basis of spatio-temporal codes.\"", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1383, - "tag": "ModelDB:123986" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 719, - "tag": "Pattern Recognition" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - } - ], - "timestamp_created": "2024-01-12 09:29:40.651637+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/123986", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "911": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 911, - "name": "Neural model of frog ventilatory rhythmogenesis (Horcholle-Bossavit and Quenet 2009)", - "repository_type": "github", - "summary": "\"In the adult frog respiratory system, periods of rhythmic movements of the buccal floor are interspersed\r\nby lung ventilation episodes. \r\n\r\nThe ventilatory activity results from the interaction of two hypothesized\r\noscillators in the brainstem. \r\n\r\nHere, we model these oscillators with two coupled neural networks, whose\r\nco-activation results in the emergence of new dynamics. \r\n\r\n..\r\nThe biological interest of this formal model is illustrated by\r\nthe persistence of the relevant dynamical features when perturbations are introduced in the model, i.e.\r\ndynamic noises and architecture modifications. \r\n\r\nThe implementation of the networks with clock-driven\r\ncontinuous time neurones provides simulations with physiological time scales.\"", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1384, - "tag": "ModelDB:123987" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - } - ], - "timestamp_created": "2024-01-12 09:29:41.256907+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/123987", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "912": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 912, - "name": "Synaptic integration in tuft dendrites of layer 5 pyramidal neurons (Larkum et al. 2009)", - "repository_type": "github", - "summary": "Simulations used in the paper. Voltage responses to current injections in different tuft locations; NMDA and calcium spike generation. Summation of multiple input distribution.", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 742, - "tag": "I p,q" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1385, - "tag": "ModelDB:124043" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 596, - "tag": "Synaptic Integration" - } - ], - "timestamp_created": "2024-01-12 09:29:41.859499+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/124043", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "913": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 913, - "name": "A network model of the vertebrate retina (Publio et al. 2009)", - "repository_type": "github", - "summary": "In this work, we use a minimal conductance-based model of the ON rod pathways in the vertebrate retina to study the effects of electrical synaptic coupling via gap junctions among rods and among AII amacrine cells on the dynamic range of the retina. The model is also used to study the effects of the maximum conductance of rod hyperpolarization activated current Ih on the dynamic range of the retina, allowing a study of the interrelations between this intrinsic membrane parameter with those two retina connectivity characteristics.", - "tags": [ - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1386, - "tag": "ModelDB:124063" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 758, - "tag": "Sensory processing" - } - ], - "timestamp_created": "2024-01-12 09:29:42.484532+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/124063", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "914": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 914, - "name": "A contracting model of the basal ganglia (Girard et al. 2008)", - "repository_type": "github", - "summary": "Basal ganglia model : selection processes between channels, dynamics controlled by contraction analysis, rate-coding model of neurons based on locally projected dynamical systems (lPDS).", - "tags": [ - { - "id": 771, - "tag": "Action Selection/Decision Making" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1387, - "tag": "ModelDB:124111" - }, - { - "id": 620, - "tag": "Python" - } - ], - "timestamp_created": "2024-01-12 09:29:43.045680+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/124111", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "915": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 915, - "name": "Dentate Gyrus Feed-forward inhibition (Ferrante et al. 2009)", - "repository_type": "github", - "summary": "In this paper, the model was used to show how that FFI can change a steeply sigmoidal input-output (I/O) curve into a double-sigmoid typical of buffer systems.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1388, - "tag": "ModelDB:124291" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 874, - "tag": "Noise Sensitivity" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 586, - "tag": "Synchronization" - } - ], - "timestamp_created": "2024-01-12 09:29:43.618473+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/124291", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "916": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 916, - "name": "Alternative time representation in dopamine models (Rivest et al. 2009)", - "repository_type": "github", - "summary": "Combines a long short-term memory (LSTM) model of the cortex to a temporal difference learning (TD) model of the basal ganglia. Code to run simulations similar to the published data: Rivest, F, Kalaska, J.F., Bengio, Y. (2009) Alternative time representation in dopamine models. Journal of Computational Neuroscience. \r\nSee http://dx.doi.org/10.1007/s10827-009-0191-1 for details.", - "tags": [ - { - "id": 808, - "tag": "Hebbian plasticity" - }, - { - "id": 778, - "tag": "Java" - }, - { - "id": 825, - "tag": "Learning" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1389, - "tag": "ModelDB:124329" - }, - { - "id": 791, - "tag": "Rate-coding model neurons" - }, - { - "id": 809, - "tag": "Reinforcement Learning" - }, - { - "id": 803, - "tag": "Unsupervised Learning" - }, - { - "id": 794, - "tag": "Working memory" - } - ], - "timestamp_created": "2024-01-12 09:29:44.208674+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/124329", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "917": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 917, - "name": "Epilepsy may be caused by very small functional changes in ion channels (Thomas et al. 2009)", - "repository_type": "github", - "summary": "We used a previously published model of the dentate gyrus with varying degrees of mossy fibre sprouting.We preformed a sensitivity analysis where we systematically varied individual properties of ion channels. The results predict that genetic variations in the properties of sodium channels are likely to have the biggest impact on network excitability. Furthermore, these changes may be as small as 1mV, which is currently undetectable using standard experimental practices.", - "tags": [ - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1390, - "tag": "ModelDB:124392" - }, - { - "id": 1379, - "tag": "parplex" - } - ], - "timestamp_created": "2024-01-12 09:29:44.878959+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/124392", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "918": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 918, - "name": "Dendritic Na+ spike initiation and backpropagation of APs in active dendrites (Nevian et al. 2007)", - "repository_type": "github", - "summary": "NEURON model used to create simulations shown in figure 6 of the paper. The model includes two point processes; one for dendritic spike initiation and the other for somatic action potential generation. The effect of filtering by imperfect recording electrode can be examined in somatic and dendritic locations.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1391, - "tag": "ModelDB:124394" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:29:45.455141+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/124394", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "919": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 919, - "name": "Dentate gyrus (Morgan et al. 2007, 2008, Santhakumar et al. 2005, Dyhrfjeld-Johnsen et al. 2007)", - "repository_type": "github", - "summary": "This model was implemented by Rob Morgan in the Soltesz lab at UC Irvine. It is a scaleable model of the rat dentate gyrus including four cell types. This model runs in serial (on a single processor) and has been published at the size of 50,000 granule cells (with proportional numbers of the other cells).", - "tags": [ - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1392, - "tag": "ModelDB:124513" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:29:46.047816+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/124513", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "920": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 920, - "name": "Reduced leech heart interneuron (Channell et al. 2009)", - "repository_type": "github", - "summary": "\"Spiking and bursting patterns of neurons are characterized by a high degree of variability.\r\n\r\nA single neuron can demonstrate endogenously various bursting patterns, changing in response to external disturbances\r\ndue to synapses, or to intrinsic factors such as channel noise.\r\n\r\nWe argue that in a model of the leech heart interneuron existing variations of bursting patterns are\r\nsignificantly enhanced by a small noise.\r\n\r\nIn the absence of noise this model shows periodic bursting with fixed\r\nnumbers of interspikes for most parameter values. ...\"\r\n", - "tags": [ - { - "id": 1393, - "tag": "CONTENT" - }, - { - "id": 1394, - "tag": "Dynamics Solver" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1395, - "tag": "ModelDB:125125" - } - ], - "timestamp_created": "2024-01-12 09:29:46.669145+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/125125", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "921": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 921, - "name": "CA1 pyramidal neuron to study INaP properties and repetitive firing (Uebachs et al. 2010)", - "repository_type": "github", - "summary": "A model of a CA1 pyramidal neuron containing a biophysically realistic morphology and 15 distributed voltage and Ca2+-dependent conductances. Repetitive firing is modulated by maximal conductance and the\r\nvoltage dependence of the persistent Na+ current (INaP).", - "tags": [ - { - "id": 795, - "tag": "ATP-senstive potassium current" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 731, - "tag": "I CAN" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 742, - "tag": "I p,q" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1396, - "tag": "ModelDB:125152" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:29:47.299437+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/125152", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "922": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 922, - "name": "Simulation studies on mechanisms of levetiracetam-mediated inhibition of IK(DR) (Huang et al. 2009)", - "repository_type": "github", - "summary": "Levetiracetam (LEV) is an S-enantiomer pyrrolidone derivative with established antiepileptic \r\nefficacy in generalized epilepsy and partial epilepsy. However, its effects on ion currents \r\nand membrane potential remain largely unclear. In this study, we investigated the effect of \r\nLEV on differentiated NG108-15 neurons. \r\n...\r\nSimulation studies in a modified \r\nHodgkin-Huxley neuron and network unraveled that the reduction of slowly inactivating IK(DR) resulted \r\nin membrane depolarization accompanied by termination of the firing of action potentials in a \r\nstochastic manner. Therefore, the inhibitory effects on slowly inactivating IK(DR) (Kv3.1-encoded \r\ncurrent) may constitute one of the underlying mechanisms through which LEV affects neuronal activity \r\nin vivo.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 769, - "tag": "I_KHT" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1397, - "tag": "ModelDB:125154" - }, - { - "id": 759, - "tag": "XPPAUT" - } - ], - "timestamp_created": "2024-01-12 09:29:47.881500+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/125154", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "923": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 923, - "name": "Maximum entropy model to predict spatiotemporal spike patterns (Marre et al. 2009)", - "repository_type": "github", - "summary": "This MATLAB code implements a model-based analysis of spike trains.\r\nThe analysis predicts the occurrence of spatio-temporal patterns of\r\nspikes in the data, and is based on a maximum entropy principle by\r\nincluding both spatial and temporal correlations. The approach is\r\napplicable to unit recordings from any region of the brain.\r\n\r\nThe code is based on Marre, et al., 2009.\r\n\r\nThe MATLAB code was written by Sami El Boustani and Olivier Marre.\r\n", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 1398, - "tag": "Maximum entropy models" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1399, - "tag": "ModelDB:125290" - } - ], - "timestamp_created": "2024-01-12 09:29:48.453727+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/125290", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "924": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 924, - "name": "Availability of low-threshold Ca2+ current in retinal ganglion cells (Lee SC et al. 2003)", - "repository_type": "github", - "summary": "\"... we measured T-type current of isolated\r\ngoldfish retinal ganglion cells with perforated-patch voltageclamp\r\nmethods in solutions containing a normal extracellular Ca2+\r\nconcentration.\r\n\r\nThe voltage sensitivities and rates of current activation,\r\ninactivation, deactivation, and recovery from inactivation were similar\r\nto those of expressed +1G (CaV3.1) Ca2+ channel clones, except that\r\nthe rate of deactivation was significantly faster.\r\n\r\nWe reproduced the\r\namplitude and kinetics of measured T currents with a numerical\r\nsimulation based on a kinetic model developed for an +1G Ca2+\r\nchannel.\r\n\r\nFinally, we show that this model predicts the increase of\r\nT-type current made available between resting potential and spike\r\nthreshold by repetitive hyperpolarizations presented at rates that are\r\nwithin the bandwidth of signals processed in situ by these neurons.\"", - "tags": [ - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1400, - "tag": "ModelDB:125378" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:29:49.036874+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/125378", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "925": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 925, - "name": "Spike propagation in dendrites with stochastic ion channels (Diba et al. 2006)", - "repository_type": "github", - "summary": "\"We investigate the effects of the stochastic nature\r\nof ion channels on the faithfulness, precision and reproducibility\r\nof electrical signal transmission in weakly active,\r\ndendritic membrane under in vitro conditions. \r\n\r\n...\r\n\r\nWe numerically simulate the effects of stochastic ion\r\nchannels on the forward and backward propagation of dendritic\r\nspikes in Monte-Carlo simulations on a reconstructed\r\nlayer 5 pyramidal neuron. \r\n\r\nWe report that in most instances\r\nthere is little variation in timing or amplitude for a single\r\nBPAP, while variable backpropagation can occur for trains\r\nof action potentials. \r\n\r\nAdditionally, we find that the generation\r\nand forward propagation of dendritic Ca2+ spikes are\r\nsusceptible to channel variability. This indicates limitations\r\non computations that depend on the precise timing of Ca2+\r\nspikes.\"", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1401, - "tag": "ModelDB:125385" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:29:49.621160+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/125385", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "926": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 926, - "name": "Respiratory central pattern generator network in mammalian brainstem (Rubin et al. 2009)", - "repository_type": "github", - "summary": "This model is a reduced version of a spatially organized respiratory central pattern generation network consisting of four neuronal populations (pre-I, early-I, post-I, and aug-E). In this reduction, each population is represented by a single neuron, in an activity-based framework (which includes the persistent sodium current for the pre-I population). The model includes three sources of external drive and can produce several experimentally observed rhythms.", - "tags": [ - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1402, - "tag": "ModelDB:125529" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 759, - "tag": "XPPAUT" - } - ], - "timestamp_created": "2024-01-12 09:29:50.226884+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/125529", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "927": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 927, - "name": "Ca2+-activated I_CAN and synaptic depression promotes network-dependent oscil. (Rubin et al. 2009)", - "repository_type": "github", - "summary": "\"... the preBotzinger complex...\r\nwe present and analyze a\r\nmathematical model demonstrating an unconventional mechanism\r\nof rhythm generation in which glutamatergic synapses and the\r\nshort-term depression of excitatory transmission play key rhythmogenic\r\nroles. \r\n\r\nRecurrent synaptic excitation triggers postsynaptic Ca2+-\r\nactivated nonspecific cation current (ICAN) to initiate a network-wide\r\nburst. \r\n\r\nRobust depolarization due to ICAN also causes voltage-dependent\r\nspike inactivation, which diminishes recurrent excitation and\r\nthus attenuates postsynaptic Ca2+ accumulation. \r\n...\"", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 776, - "tag": "C or Cplusplus program (web link to model)" - }, - { - "id": 731, - "tag": "I CAN" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1403, - "tag": "ModelDB:125649" - }, - { - "id": 1404, - "tag": "NeuronetExperimenter (web link to model)" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 830, - "tag": "XPP (web link to model)" - } - ], - "timestamp_created": "2024-01-12 09:29:50.872412+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/125649", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "928": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 928, - "name": "Ca2+ current versus Ca2+ channel cooperativity of exocytosis (Matveev et al. 2009)", - "repository_type": "github", - "summary": "\"... While varying extracellular or intracellular Ca2+ concentration assesses the intrinsic biochemical Ca2+ cooperativity of \r\nneurotransmitter release, varying the number of open Ca2+ channels using pharmacological channel block or the tail current \r\ntitration probes the cooperativity between individual Ca2+ channels in triggering exocytosis. \r\n...\r\n\r\nHere we provide a detailed analysis of the Ca2+ sensitivity measures probed by these experimental\r\nprotocols, present simple expressions for special cases, and demonstrate the distinction between the Ca2+ current cooperativity, defined\r\nby the relationship between exocytosis rate and the whole-terminal Ca2+ current magnitude, and the underlying Ca2+ channel cooperativity,\r\ndefined as the average number of channels involved in the release of a single vesicle. \r\n...\r\nFurther, we use three-dimensional computational modeling of buffered Ca2+\r\ndiffusion to analyze these distinct Ca2+ cooperativity measures, and demonstrate the role of endogenous Ca2+ buffers on such measures.\r\n\r\nWe show that buffers can either increase or decrease the Ca2+ current cooperativity of exocytosis, depending on their concentration and\r\nthe single-channel Ca2+ current.\"", - "tags": [ - { - "id": 869, - "tag": "CalC Calcium Calculator (web link to model)" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1405, - "tag": "ModelDB:125676" - } - ], - "timestamp_created": "2024-01-12 09:29:51.443014+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/125676", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "929": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 929, - "name": "Deterministic chaos in a mathematical model of a snail neuron (Komendantov and Kononenko 1996)", - "repository_type": "github", - "summary": "\"Chaotic regimes in a mathematical model of pacemaker activity in the bursting neurons of a snail Helix pomatia, have been investigated.\r\n\r\nThe model includes a slow-wave generating mechanism, a spike-generating mechanism, an inward Ca current, intracellular Ca ions, [Ca2+]in, their fast buffering and uptake by intracellular Ca stores, and a [Ca2+]in-inhibited Ca current.\r\n\r\nChemosensitive voltage-activated conductance, gB*, responsible for termination of the spike burst, and chemosensitive sodium conductance, gNa*, responsible for the depolarization phase of the slow-wave, were used as control parameters.\r\n...\r\nTime courses of the membrane potential and [Ca2+]in were employed to analyse different regimes in the model. \r\n...\"", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1406, - "tag": "ModelDB:125683" - }, - { - "id": 759, - "tag": "XPPAUT" - } - ], - "timestamp_created": "2024-01-12 09:29:52.013657+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/125683", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "930": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 930, - "name": "Balance of excitation and inhibition (Carvalho and Buonomano 2009)", - "repository_type": "github", - "summary": "\" ... \r\nHere, theoretical analyses reveal that excitatory synaptic\r\nstrength controls the threshold of the neuronal\r\ninput-output function, while inhibitory plasticity\r\nalters the threshold and gain.\r\n\r\nExperimentally, changes in the balance of excitation and inhibition\r\nin CA1 pyramidal neurons also altered their input-output\r\nfunction as predicted by the model.\r\n\r\nThese results support the existence of two functional\r\nmodes of plasticity that can be used to optimize\r\ninformation processing: threshold and gain plasticity.\"", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1407, - "tag": "ModelDB:125689" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 596, - "tag": "Synaptic Integration" - } - ], - "timestamp_created": "2024-01-12 09:29:52.617091+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/125689", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "931": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 931, - "name": "Hippocampus CA1: Temporal sensitivity of signaling pathways underlying LTP (Kim et al. 2010)", - "repository_type": "github", - "summary": "Temporal sensitivity of signaling pathways underlying L-LTP. Single compartment, deterministic model of calcium and dopamine activated pathways, leading to CaMKII and PKA activation. Experimental verification of model prediction.", - "tags": [ - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1408, - "tag": "ModelDB:125733" - }, - { - "id": 751, - "tag": "Signaling pathways" - }, - { - "id": 759, - "tag": "XPPAUT" - } - ], - "timestamp_created": "2024-01-12 09:29:53.313541+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/125733", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "932": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 932, - "name": "Calcium waves in neuroblastoma cells (Fink et al. 2000)", - "repository_type": "github", - "summary": "Uses a model of IP3-mediated release of Ca from endoplasmic reticulum (ER) to study how initiation and propagation of Ca waves are affected by cell geometry, spatial distributions of ER and IP3 generation, and diffusion of Ca and mobile buffer.\r\n", - "tags": [ - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 790, - "tag": "Calcium waves" - }, - { - "id": 818, - "tag": "I_SERCA" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1409, - "tag": "ModelDB:125745" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 782, - "tag": "Virtual Cell (web link to model)" - } - ], - "timestamp_created": "2024-01-12 09:29:54.003382+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/125745", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "933": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 933, - "name": "Huntington`s disease model (Gambazzi et al. 2010)", - "repository_type": "github", - "summary": "\"Although previous studies of Huntington\u2019s disease (HD) have addressed many potential mechanisms of striatal neuron dysfunction and death, it is also known based on clinical findings that cortical function is dramatically disrupted in HD. With respect to disease etiology, however, the specific molecular and neuronal circuit bases for the cortical effects of mutant huntingtin (htt) have remained largely unknown. \r\n\r\nIn the present work we studied the relation between the molecular effects of mutant htt fragments in cortical cells and the corresponding behavior of cortical neuron microcircuits using a novel cellular model of HD. We observed that a transcript-selective diminution in activity-dependent BDNF expression preceded the onset of a synaptic connectivity deficit in ex vivo cortical networks, which manifested as decreased spontaneous collective burst-firing behavior measured by multi-electrode array substrates. Decreased BDNF expression was determined to be a significant contributor to network-level dysfunction, as shown by the ability of exogenous BDNF to ameliorate cortical microcircuit burst firing. \r\n\r\nThe molecular determinants of the dysregulation of activity-dependent BDNF expression by mutant htt appear to be distinct from previously elucidated mechanisms, as they do not involve known NRSF/REST-regulated promoter sequences, but instead result from dysregulation of BDNF exon IV and VI transcription. These data elucidate a novel HD-related deficit in BDNF gene regulation as a plausible mechanism of cortical neuron hypoconnectivity and cortical function deficits in HD. Moreover, the novel model paradigm established here is well-suited to further mechanistic and drug screening research applications.\r\n\r\nA simple mathematical model is proposed to interpret the observations and to explore the impact of specific synaptic dysfunctions on network activity. Interestingly, the model predicts a decrease in synaptic connectivity to be an early effect of mutant huntingtin in cortical neurons, supporting the hypothesis of decreased, rather than increased, synchronized cortical firing in HD.\"", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 1410, - "tag": "Huntington's" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1411, - "tag": "ModelDB:125748" - }, - { - "id": 787, - "tag": "Pathophysiology" - } - ], - "timestamp_created": "2024-01-12 09:29:54.581683+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/125748", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "934": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 934, - "name": "Long-term adaptation with power-law dynamics (Zilany et al. 2009)", - "repository_type": "github", - "summary": "...\r\nA model of rate adaptation at the synapse between inner hair cells and auditory-nerve\r\n(AN) fibers that includes both exponential and power-law dynamics is presented here.\r\n\r\nExponentially\r\nadapting components with rapid and short-term time constants, which are mainly responsible for\r\nshaping onset responses, are followed by two parallel paths with power-law adaptation that provide\r\nslowly and rapidly adapting responses.\r\n\r\n...\r\nThe proposed model is capable of accurately predicting several sets of AN data,\r\nincluding amplitude-modulation transfer functions, long-term adaptation, forward masking, and\r\nadaptation to increments and decrements in the amplitude of an ongoing stimulus.", - "tags": [ - { - "id": 762, - "tag": "Audition" - }, - { - "id": 776, - "tag": "C or Cplusplus program (web link to model)" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1412, - "tag": "ModelDB:125855" - } - ], - "timestamp_created": "2024-01-12 09:29:55.144992+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/125855", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "935": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 935, - "name": "A Fast Rhythmic Bursting Cell: in vivo cell modeling (Lee 2007)", - "repository_type": "github", - "summary": "One of the cellular mechanisms underlying the generation of gamma oscillations is a type of cortical pyramidal neuron named fast rhythmic bursting (FRB) cells. After cells from cats' primary visual cortices were filled with Neurobiotin, the brains were cut, and the cells were photographed. One FRB cell was chosen to be confocaled, reconstructed with Neurolucida software, and generated a detailed multi-compartmental model in the NEURON program. We explore firing properties of FRB cells and the role of enhanced Na+ conductance.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 861, - "tag": "I_K,Na" - }, - { - "id": 817, - "tag": "I_Na,Ca" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1413, - "tag": "ModelDB:125857" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 726, - "tag": "Vision" - } - ], - "timestamp_created": "2024-01-12 09:29:55.770826+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/125857", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "936": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 936, - "name": "Integrate and fire model code for spike-based coincidence-detection (Heinz et al. 2001, others)", - "repository_type": "github", - "summary": "Model code relevant to three papers; two on level discrimination and one on masked detection at low frequencies.", - "tags": [ - { - "id": 762, - "tag": "Audition" - }, - { - "id": 776, - "tag": "C or Cplusplus program (web link to model)" - }, - { - "id": 595, - "tag": "Coincidence Detection" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1414, - "tag": "ModelDB:126052" - }, - { - "id": 569, - "tag": "Simplified Models" - } - ], - "timestamp_created": "2024-01-12 09:29:56.340535+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/126052", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "937": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 937, - "name": "Model of neural responses to amplitude-modulated tones (Nelson and Carney 2004)", - "repository_type": "github", - "summary": "\"A phenomenological model with time-varying excitation and inhibition was developed to study\r\npossible neural mechanisms underlying changes in the representation of temporal envelopes along\r\nthe auditory pathway. A modified version of an existing auditory-nerve model (Zhang et al., J.\r\nAcoust. Soc. Am. 109, 648\u2013670 (2001) was used to provide inputs to higher hypothetical\r\nprocessing centers. \r\n\r\nModel responses were compared directly to published physiological data at three\r\nlevels: the auditory nerve, ventral cochlear nucleus, and inferior colliculus.\r\n...\"", - "tags": [ - { - "id": 762, - "tag": "Audition" - }, - { - "id": 776, - "tag": "C or Cplusplus program (web link to model)" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1415, - "tag": "ModelDB:126096" - }, - { - "id": 586, - "tag": "Synchronization" - } - ], - "timestamp_created": "2024-01-12 09:29:56.917791+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/126096", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "938": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 938, - "name": "A kinetic model of dopamine- and calcium-dependent striatal synaptic plasticity (Nakano et al. 2010)", - "repository_type": "github", - "summary": "A signaling pathway model of spines that express D1-type dopamine receptors was constructed to analyze the dynamic mechanisms of dopamine- and calcium-dependent plasticity. \r\n\r\nThe model incorporated all major signaling molecules, including dopamine- and cyclic AMP-regulated phosphoprotein with a molecular weight of 32 kDa (DARPP32), as well as AMPA receptor trafficking in the post-synaptic membrane. Simulations with dopamine and calcium inputs reproduced dopamine- and calcium-dependent plasticity. \r\n\r\nFurther in silico experiments revealed that the positive feedback loop consisted of protein kinase A (PKA), protein phosphatase 2A (PP2A), and the phosphorylation site at threonine 75 of DARPP-32 (Thr75) served as the major switch for inducing LTD and LTP. \r\n\r\nThe present model elucidated the mechanisms involved in bidirectional regulation of corticostriatal synapses and will allow for further exploration into causes and therapies for dysfunctions such as drug addiction.\"", - "tags": [ - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1416, - "tag": "ModelDB:126098" - }, - { - "id": 751, - "tag": "Signaling pathways" - } - ], - "timestamp_created": "2024-01-12 09:29:57.470876+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/126098", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "939": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 939, - "name": "Encoding and discrimination of vowel-like sounds (Tan and Carney 2005)", - "repository_type": "github", - "summary": "\"The sensitivity of listeners to changes in the center frequency of vowel-like harmonic complexes as\r\na function of the center frequency of the complex cannot be explained by changes in the level of the\r\nstimulus [Lyzenga and Horst, J. Acoust. Soc. Am. 98, 1943\u20131955 (1995)]. \r\n\r\nRather, a complex pattern\r\nof sensitivity is seen; for a spectrum with a triangular envelope, the greatest sensitivity occurs when\r\nthe center frequency falls between harmonics, whereas for a spectrum with a trapezoidal envelope,\r\ngreatest sensitivity occurs when the center frequency is aligned with a harmonic.\r\n\r\nIn this study, the\r\nthresholds of a population model of auditory-nerve (AN) fibers were quantitatively compared to\r\nthese trends in psychophysical thresholds.\r\n\r\nSingle-fiber and population model responses were\r\nevaluated in terms of both average discharge rate and the combination of rate and timing\r\ninformation.\r\n...\"", - "tags": [ - { - "id": 762, - "tag": "Audition" - }, - { - "id": 776, - "tag": "C or Cplusplus program (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1417, - "tag": "ModelDB:126371" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - } - ], - "timestamp_created": "2024-01-12 09:29:58.042759+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/126371", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "940": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 940, - "name": "Auditory nerve spontaneous rate histograms (Jackson and Carney 2005)", - "repository_type": "github", - "summary": "Histograms of spontaneous rate estimates of auditory nerve are well reproduced by models with two or three spontaneous rates and long range dependence.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 762, - "tag": "Audition" - }, - { - "id": 776, - "tag": "C or Cplusplus program (web link to model)" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1418, - "tag": "ModelDB:126389" - } - ], - "timestamp_created": "2024-01-12 09:29:58.606050+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/126389", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "941": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 941, - "name": "Response properties of an integrate and fire model (Zhang and Carney 2005)", - "repository_type": "github", - "summary": "\"A computational technique is described for calculation of the interspike interval and poststimulus time histograms for the responses of an\r\nintegrate-and-fire model to arbitrary inputs.\r\n...\r\nFor stationary inputs, the regularity of the output was studied in detail for various model parameters.\r\n\r\nFor nonstationary inputs, the effects of the model parameters on the output synchronization index were explored.\r\n\r\n... these response properties have been reported for some cells in the ventral cochlear nucleus in the auditory brainstem.\r\n\"", - "tags": [ - { - "id": 762, - "tag": "Audition" - }, - { - "id": 776, - "tag": "C or Cplusplus program (web link to model)" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1419, - "tag": "ModelDB:126392" - } - ], - "timestamp_created": "2024-01-12 09:29:59.227288+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/126392", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "942": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 942, - "name": "Spike-Timing-Based Computation in Sound Localization (Goodman and Brette 2010)", - "repository_type": "github", - "summary": "\" ...\r\nIn neuron models consisting of spectro-temporal filtering and spiking\r\nnonlinearity, we found that the binaural structure induced by spatialized sounds is mapped to synchrony patterns that\r\ndepend on source location rather than on source signal. \r\n\r\nLocation-specific synchrony patterns would then result in the\r\nactivation of location-specific assemblies of postsynaptic neurons. \r\n\r\nWe designed a spiking neuron model which exploited\r\nthis principle to locate a variety of sound sources in a virtual acoustic environment using measured human head-related\r\ntransfer functions.\r\n...\"", - "tags": [ - { - "id": 645, - "tag": "Brian" - }, - { - "id": 595, - "tag": "Coincidence Detection" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1420, - "tag": "ModelDB:126465" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 586, - "tag": "Synchronization" - } - ], - "timestamp_created": "2024-01-12 09:29:59.822268+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/126465", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "943": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 943, - "name": "Asynchronous irregular and up/down states in excitatory and inhibitory NNs (Destexhe 2009)", - "repository_type": "github", - "summary": "\"Randomly-connected networks of integrate-and-fire (IF) neurons are known to display asynchronous irregular (AI) activity states, which resemble the discharge activity recorded in the cerebral cortex of awake animals. \r\n\r\n...\r\nHere, we investigate the occurrence of AI states in networks of nonlinear IF neurons, such as the adaptive exponential IF (Brette-Gerstner-Izhikevich) model. This model can display intrinsic properties such as low-threshold spike (LTS), regular spiking (RS) or fast-spiking (FS). We successively investigate the oscillatory and AI dynamics of thalamic, cortical and thalamocortical networks using such models.\r\n...\"", - "tags": [ - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1421, - "tag": "ModelDB:126466" - }, - { - "id": 714, - "tag": "PyNN" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - } - ], - "timestamp_created": "2024-01-12 09:30:00.410372+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/126466", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "944": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 944, - "name": "Cardiac sarcomere dynamics (Negroni and Lascano 1996)", - "repository_type": "github", - "summary": "\"A muscle model establishing the link between cross-bridge dynamics and intracellular Ca2+ kinetics was assessed by simulation of experiments performed in isolated cardiac muscle.\r\n\r\nThe model is composed by the series arrangement of muscle units formed by inextensible thick and thin filaments in parallel with an elastic element.\r\n\r\nAttached cross-bridges act as independent force generators whose force is linearly related to the elongation of their elastic structure.\r\n\r\nCa2+ kinetics is described by a four-state system of sites on the thin filament associated with troponin C: sites with free troponin C (T), sites with Ca2+ bound to troponin C (TCa); sites with Ca2+ bound to troponin C and attached cross-bridges (TCa*); and sites with troponin C not associated with Ca2+ and attached cross-bridges (T*).\r\n\r\nThe intracellular Ca2+ concentration ([Ca2+]) is controlled solely by the sarcoplasmic reticulum through an inflow function and a saturated outflow pump function.\r\n...\"", - "tags": [ - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1422, - "tag": "ModelDB:126467" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:30:00.973351+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/126467", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "945": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 945, - "name": "Formation of synfire chains (Jun and Jin 2007)", - "repository_type": "github", - "summary": "\"Temporally precise sequences of neuronal spikes that span hundreds of milliseconds are observed in many brain areas,\r\nincluding songbird premotor nucleus, cat visual cortex, and primary motor cortex.\r\n\r\nSynfire chains\u2014networks in which groups of\r\nneurons are connected via excitatory synapses into a unidirectional chain\u2014are thought to underlie the generation of such\r\nsequences. \r\n\r\nIt is unknown, however, how synfire chains can form in local neural circuits, especially for long chains. \r\n\r\nHere, we\r\nshow through computer simulation that long synfire chains can develop through spike-time dependent synaptic plasticity and\r\naxon remodeling\u2014the pruning of prolific weak connections that follows the emergence of a finite number of strong\r\nconnections. \r\n...\"", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1423, - "tag": "ModelDB:126471" - }, - { - "id": 802, - "tag": "STDP" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 726, - "tag": "Vision" - } - ], - "timestamp_created": "2024-01-12 09:30:01.532020+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/126471", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "946": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 946, - "name": "Models analysis for auditory-nerve synapse (Zhang and Carney 2005)", - "repository_type": "github", - "summary": "\"A general mathematical approach was proposed to study phenomenological models of the\r\ninner-hair-cell and auditory-nerve (AN) synapse complex. Two models (Meddis, 1986; Westerman\r\nand Smith, 1988) were studied using this unified approach. The responses of both models to a\r\nconstant-intensity stimulus were described mathematically, and the relationship between model\r\nparameters and response characteristics was investigated. ...\". The paper then modifies these\r\nto make a more physiologically realistic model.\r\n", - "tags": [ - { - "id": 762, - "tag": "Audition" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1424, - "tag": "ModelDB:126489" - } - ], - "timestamp_created": "2024-01-12 09:30:02.113583+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/126489", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "947": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 947, - "name": "Predicting formant-frequency discrimination in noise (Tan and Carney 2006)", - "repository_type": "github", - "summary": "\"To better understand how the auditory system extracts speech signals in the presence of noise,\r\ndiscrimination thresholds for the second formant frequency were predicted with simulations of\r\nauditory-nerve responses.\r\n\r\nThese predictions employed either average-rate information or combined\r\nrate and timing information, and either populations of model fibers tuned across a wide range of\r\nfrequencies or a subset of fibers tuned to a restricted frequency range.\r\n\r\nIn general, combined temporal\r\nand rate information for a small population of model fibers tuned near the formant frequency was\r\nmost successful in replicating the trends reported in behavioral data for formant-frequency\r\ndiscrimination. ...\"", - "tags": [ - { - "id": 762, - "tag": "Audition" - }, - { - "id": 776, - "tag": "C or Cplusplus program (web link to model)" - }, - { - "id": 595, - "tag": "Coincidence Detection" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1425, - "tag": "ModelDB:126598" - } - ], - "timestamp_created": "2024-01-12 09:30:02.849069+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/126598", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "948": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 948, - "name": "Models for diotic and dichotic detection (Davidson et al. 2009)", - "repository_type": "github", - "summary": "Several psychophysical models for masked detection were evaluated using reproducible noises.\r\n\r\nThe data were hit and false-alarm rates from three psychophysical studies of detection of 500-Hz tones\r\nin reproducible noise under diotic (N0S0) and dichotic (N0Spi) conditions with four stimulus\r\nbandwidths (50, 100, 115, and 2900 Hz).\r\n\r\nDiotic data were best predicted by an energy-based multiple-detector model that linearly combined stimulus energies at the outputs of several\r\ncritical-band filters.\r\n\r\nThe tone-plus-noise trials in the dichotic data were best predicted by models that\r\nlinearly combined either the average values or the standard deviations of interaural time and level\r\ndifferences; however, these models offered no predictions for noise-alone responses.\r\n\r\n...\". The Breebart et al. 2001 and the Dau et al. 1996 models are supplied at the Carney lab web site.", - "tags": [ - { - "id": 762, - "tag": "Audition" - }, - { - "id": 776, - "tag": "C or Cplusplus program (web link to model)" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1426, - "tag": "ModelDB:126636" - } - ], - "timestamp_created": "2024-01-12 09:30:03.603981+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/126636", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "949": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 949, - "name": "A simplified cerebellar Purkinje neuron (the PPR model) (Brown et al. 2011)", - "repository_type": "github", - "summary": "These models were implemented in NEURON by Sherry-Ann Brown in the laboratory of Leslie M. Loew.\r\n\r\nThe files reproduce Figures 2c-f from Brown et al, 2011 \"Virtual NEURON: a Strategy For Merged Biochemical and Electrophysiological Modeling\".\r\n", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 742, - "tag": "I p,q" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 763, - "tag": "Intrinsic plasticity" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1427, - "tag": "ModelDB:126637" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 596, - "tag": "Synaptic Integration" - } - ], - "timestamp_created": "2024-01-12 09:30:04.199531+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/126637", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "950": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 950, - "name": "NAcc medium spiny neuron: effects of cannabinoid withdrawal (Spiga et al. 2010)", - "repository_type": "github", - "summary": "Cannabinoid withdrawal produces a hypofunction of dopaminergic neurons targeting medium spiny neurons (MSN) of the forebrain. Administration of a CB1 receptor antagonist to control rats provoked structural abnormalities, reminiscent of those observed in withdrawal conditions and support the regulatory role of cannabinoids in neurogenesis, axonal growth and synaptogenesis. Experimental observations were incorporated into a realistic computational model which predicts a strong reduction in the excitability of morphologically-altered MSN, yielding a significant reduction in action potential output. These paper provided direct morphological evidence for functional abnormalities associated with cannabinoid dependence at the level of dopaminergic neurons and their post synaptic counterpart, supporting a hypodopaminergic state as a distinctive feature of the \u201caddicted brain\u201d.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 841, - "tag": "Addiction" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 842, - "tag": "I Krp" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1428, - "tag": "ModelDB:126640" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 596, - "tag": "Synaptic Integration" - } - ], - "timestamp_created": "2024-01-12 09:30:04.797354+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/126640", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "951": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 951, - "name": "CA1 pyramidal neuron: rebound spiking (Ascoli et al.2010)", - "repository_type": "github", - "summary": "The model demonstrates that CA1 pyramidal neurons support rebound spikes mediated by hyperpolarization-activated inward current (Ih), and normally masked by A-type potassium channels (KA). Partial KA reduction confined to one or few branches of the apical tuft may be sufficient to elicit a local spike following a train of synaptic inhibition. These data suggest that the plastic regulation of KA can provide a dynamic switch to unmask post-inhibitory spiking in CA1 pyramidal neurons, further increasing the signal processing power of the CA1 synaptic microcircuitry.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 763, - "tag": "Intrinsic plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1429, - "tag": "ModelDB:126776" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 596, - "tag": "Synaptic Integration" - } - ], - "timestamp_created": "2024-01-12 09:30:05.430005+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/126776", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "952": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 952, - "name": "CA3 pyramidal neuron (Safiulina et al. 2010)", - "repository_type": "github", - "summary": "In this review some of the recent work carried out in our laboratory concerning the functional\r\nrole of GABAergic signalling at immature mossy fibres (MF)-CA3 principal cell synapses has\r\nbeen highlighted. To compare the relative strength of CA3 pyramidal cell\r\noutput in relation to their MF glutamatergic or GABAergic inputs in postnatal\r\ndevelopment, a realistic model was constructed taking into account the different\r\nbiophysical properties of these synapses.\r\n", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1430, - "tag": "ModelDB:126814" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 777, - "tag": "Spike Frequency Adaptation" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - } - ], - "timestamp_created": "2024-01-12 09:30:06.096654+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/126814", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "953": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 953, - "name": "CN bushy, stellate neurons (Rothman, Manis 2003) (Brian)", - "repository_type": "github", - "summary": "Cochlear neuron model of Rothman & Manis (2003). Adapted from the Neuron implementation.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 645, - "tag": "Brian" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1431, - "tag": "ModelDB:126899" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - } - ], - "timestamp_created": "2024-01-12 09:34:36.611736+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/126899", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "954": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 954, - "name": "Alcohol excites Cerebellar Golgi Cells by inhibiting the Na+/K+ ATPase (Botta et al.2010)", - "repository_type": "github", - "summary": "Patch-clamp in cerebellar slices and computer modeling show that ethanol excites Golgi cells by inhibiting the Na+/K+ ATPase. In particular, voltage-clamp recordings of Na+/K+ ATPase currents indicated that ethanol partially inhibits this pump and this effect could be mimicked by low concentrations of the Na+/K+ ATPase blocker ouabain. The partial inhibition of Na+/K+ ATPase in a computer model of the Golgi cell reproduced these experimental findings that established a novel mechanism of action of ethanol on neural excitability.", - "tags": [ - { - "id": 839, - "tag": "Alcohol Use Disorder" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 748, - "tag": "Markov-type model" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1432, - "tag": "ModelDB:127021" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 740, - "tag": "Na/K pump" - } - ], - "timestamp_created": "2024-01-12 09:34:37.173882+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/127021", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "955": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 955, - "name": "Control of vibrissa motoneuron firing (Harish and Golomb 2010)", - "repository_type": "github", - "summary": "We construct and analyze a single-compartment, conductance-based model of vibrissa motoneurons. Low firing rates are supported in extended regimes by adaptation currents and the minimal firing rate decreases with the persistent sodium conductance gNaP and increases with M-potassium \r\nand h-cation conductances. Suprathreshold resonance results from the locking properties of vMN firing to stimuli and from reduction of firing rates at low frequencies by slow M and afterhyperpolarization potassium conductances. h conductance only slightly affects the suprathreshold resonance. When a vMN is subjected to a small periodic CPG input, serotonergically induced gNaP elevation may transfer the system from quiescence to a firing state that is highly\r\nlocked to the CPG input.\r\n", - "tags": [ - { - "id": 576, - "tag": "I K" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 1433, - "tag": "I_AHP" - }, - { - "id": 1434, - "tag": "Locking, mixed mode" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1435, - "tag": "ModelDB:127022" - }, - { - "id": 759, - "tag": "XPPAUT" - } - ], - "timestamp_created": "2024-01-12 09:34:37.796393+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/127022", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "956": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 956, - "name": "Pipette and membrane patch geometry effects on GABAa currents patch-clamp exps (Moroni et al. 2011)", - "repository_type": "github", - "summary": "Ion currents, mediated by GABAa-receptors in outside-out membrane patches, may alter the concentration of Chloride ions inside the pipette and the membrane patch.\r\n\r\nGABAa-receptors are in fact ionotropic synaptic receptors, selective to Chloride ions. Therefore, chloride fluxes across the membrane patch correlate to GABAa-receptor opening.\r\n\r\nChloride ions accumulation, depletion and diffusion, inside the pipette and the membrane patch, affect by definition the Chloride equilibrium (i.e. Nernst) electrical potential. This in turn changes the ionic driving force underlying GABAa-mediated currents.\r\n\r\nIt follows that, in case of very small volumes and confined geometries, voltage-clamp recordings of GABAa-receptor currents carry information on both i) Chloride diffusion and ii) receptor kinetics.\r\n\r\nThe relevance of (i) and (ii) have been studied numerically by defining a 1-dimensional biophysical model, released here to the interested user.", - "tags": [ - { - "id": 765, - "tag": "I Chloride" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1436, - "tag": "ModelDB:127190" - } - ], - "timestamp_created": "2024-01-12 09:34:38.402351+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/127190", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "957": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 957, - "name": "Network topologies for producing limited sustained activation (Kaiser and Hilgetag 2010)", - "repository_type": "github", - "summary": "Uses networks of cellular automata to test hypotheses about network topologies that can produce limited, sustained activity. Inspired by empirically-based ideas about neocortical architecture, but conceived and implemented at a level of abstraction that is not closely linked to empirical observations.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 855, - "tag": "Connectivity matrix" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1437, - "tag": "ModelDB:127192" - } - ], - "timestamp_created": "2024-01-12 09:34:39.047958+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/127192", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "958": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 958, - "name": "Spatial summation of excitatory and inhibitory inputs in pyramidal neurons (Hao et al. 2010)", - "repository_type": "github", - "summary": "\"... Based on realistic modeling and experiments in rat hippocampal\r\nslices, we derived a simple arithmetic rule for spatial summation\r\nof concurrent excitatory glutamatergic inputs (E) and inhibitory\r\nGABAergic inputs (I). \r\n\r\nThe somatic response can be well approximated\r\nas the sum of the excitatory postsynaptic potential (EPSP), the inhibitory\r\npostsynaptic potential (IPSP), and a nonlinear term proportional\r\nto their product (k*EPSP*IPSP), where the coefficient k reflects the\r\nstrength of shunting effect.\r\n...\"", - "tags": [ - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1438, - "tag": "ModelDB:127305" - }, - { - "id": 743, - "tag": "NEURON (web link to model)" - }, - { - "id": 596, - "tag": "Synaptic Integration" - } - ], - "timestamp_created": "2024-01-12 09:34:39.646447+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/127305", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "959": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 959, - "name": "A model of the temporal pattern generator of C. elegans egg-laying behavior (Zhang et. al 2010)", - "repository_type": "github", - "summary": "\"... We suggest that the HSN neuron is the executive neuron driving egg-laying events. We propose that the VC neurons act as \"single egg counters\" that inhibit HSN activity for short periods in response to individual egg-laying events. We further propose that the uv1 neuroendocrine cells are \"cluster counters\", which inhibit HSN activity for longer periods and are responsible for the time constant of the inactive phase. Together they form an integrated circuit that drives the clustered egg-laying pattern. ...\"", - "tags": [ - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1439, - "tag": "ModelDB:127321" - }, - { - "id": 1440, - "tag": "R (web link to model)" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - } - ], - "timestamp_created": "2024-01-12 09:34:40.134218+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/127321", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "960": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 960, - "name": "A two-stage model of dendritic integration in CA1 pyramidal neurons (Katz et al. 2009)", - "repository_type": "github", - "summary": "\"... In a two-stage integration model, inputs contribute directly to dendritic spikes, and outputs from multiple branches sum in the axon. ... We used serial-section electron microscopy to reconstruct individual apical oblique dendritic branches of CA1 pyramidal neurons and observe a synapse distribution consistent with the two-stage integration model. Computational modeling suggests that the observed synapse distribution enhances the contribution of each dendritic branch to neuronal output.\"", - "tags": [ - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1441, - "tag": "ModelDB:127351" - }, - { - "id": 743, - "tag": "NEURON (web link to model)" - }, - { - "id": 596, - "tag": "Synaptic Integration" - } - ], - "timestamp_created": "2024-01-12 09:34:40.626197+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/127351", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "961": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 961, - "name": "Prediction for the presence of voltage-gated Ca2+ channels in myelinated central axons (Brown 2003)", - "repository_type": "github", - "summary": "\"The objective of this current study was to investigate whether voltage gated Ca(2+) channels are present \r\non axons of the adult rat optic nerve (RON).\r\n\r\nSimulations of axonal excitability using a Hodgkin-Huxley based \r\none-compartment model incorporating I(Na), I(K) and leak currents were used to predict conditions under \r\nwhich the potential contribution of a Ca(2+) current to an evoked action potential could be measured. \r\n\r\n... , as predicted by the simulation, reducing the repolarizing effect of I(K) by adding the \r\nK(+) channel blocker 4-AP revealed a Ca(2+) component on the repolarizing phase of the action potential that \r\nwas blocked by the Ca(2+) channel inhibitor nifedipine.\"\r\n", - "tags": [ - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1442, - "tag": "ModelDB:127355" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 732, - "tag": "Tutorial/Teaching" - }, - { - "id": 759, - "tag": "XPPAUT" - } - ], - "timestamp_created": "2024-01-12 09:34:41.184227+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/127355", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "962": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 962, - "name": "Basal ganglia network model of subthalamic deep brain stimulation (Hahn and McIntyre 2010)", - "repository_type": "github", - "summary": "Basal ganglia network model of parkinsonian activity and subthalamic deep brain stimulation in non-human primates from the article \r\n\r\nInstructions are provided in the README.txt file. Contact hahnp@ccf.org if you have any questions about the implementation of the model. Please include \"ModelDB - BGnet\" in the subject heading.", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 831, - "tag": "Deep brain stimulation" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1443, - "tag": "ModelDB:127388" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 801, - "tag": "Parkinson's" - } - ], - "timestamp_created": "2024-01-12 09:34:41.689980+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/127388", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "963": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 963, - "name": "Regulation of firing frequency in a midbrain dopaminergic neuron model (Kuznetsova et al. 2010)", - "repository_type": "github", - "summary": "A dopaminergic (DA) neuron model with a morphologicaly realistic dendritic architecture. The model captures several salient features of DA neurons under different pharmacological manipulations and exhibits depolarization block for sufficiently high current pulses applied to the soma.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1444, - "tag": "ModelDB:127507" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 740, - "tag": "Na/K pump" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - } - ], - "timestamp_created": "2024-01-12 09:34:42.206242+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/127507", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "964": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 964, - "name": "Temporal and spatial characteristics of vibrissa responses to motor commands (Simony et al. 2010)", - "repository_type": "github", - "summary": "\"A mechanistic description of the generation of whisker movements is essential for understanding the control of whisking and vibrissal\r\nactive touch. We explore how facial-motoneuron spikes are translated, via an intrinsic muscle, to whisker movements. This is achieved by\r\nconstructing, simulating, and analyzing a computational, biomechanical model of the motor plant, and by measuring spiking to movement\r\ntransformations at small and large angles using high-precision whisker tracking in vivo. \r\n...\r\nThe model provides a direct translation from motoneuron spikes to whisker movements\r\nand can serve as a building block in closed-loop motor\u2013sensory models of active touch.\"", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1445, - "tag": "ModelDB:127512" - } - ], - "timestamp_created": "2024-01-12 09:34:43.141964+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/127512", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "965": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 965, - "name": "Comparison of full and reduced globus pallidus models (Hendrickson 2010)", - "repository_type": "github", - "summary": "In this paper, we studied what features of realistic full model activity patterns can and cannot be preserved by morphologically reduced models. To this end, we reduced the morphological complexity of a full globus pallidus neuron model possessing active dendrites and compared its spontaneous and driven responses to those of the reduced models.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1446, - "tag": "ModelDB:127728" - }, - { - "id": 791, - "tag": "Rate-coding model neurons" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 596, - "tag": "Synaptic Integration" - } - ], - "timestamp_created": "2024-01-12 09:34:43.721480+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/127728", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "966": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 966, - "name": "Global structure, robustness, and modulation of neuronal models (Goldman et al. 2001)", - "repository_type": "github", - "summary": "\"The electrical characteristics of many neurons are remarkably\r\nrobust in the face of changing internal and external conditions.\r\n\r\nAt the same time, neurons can be highly sensitive to neuromodulators.\r\n\r\nWe find correlates of this dual robustness and\r\nsensitivity in a global analysis of the structure of a\r\nconductance-based model neuron.\r\n...\"", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1447, - "tag": "ModelDB:127878" - }, - { - "id": 823, - "tag": "Phase Response Curves" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - } - ], - "timestamp_created": "2024-01-12 09:34:44.252059+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/127878", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "967": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 967, - "name": "Spectral method and high-order finite differences for nonlinear cable (Omurtag and Lytton 2010)", - "repository_type": "github", - "summary": "We use high-order approximation schemes for the space derivatives in the nonlinear cable equation and investigate the behavior of numerical solution errors by using exact solutions, where available, and grid convergence. The space derivatives are numerically approximated by means of differentiation matrices. A flexible form for the injected current is used that can be adjusted smoothly from a very broad to a narrow peak, which leads, for the passive cable, to a simple, exact solution. We provide comparisons with exact solutions in an unbranched passive cable, the convergence of solutions with progressive refinement of the grid in an active cable, and the simulation of spike initiation in a biophysically realistic single-neuron model.\r\n", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 861, - "tag": "I_K,Na" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1448, - "tag": "ModelDB:127887" - } - ], - "timestamp_created": "2024-01-12 09:34:44.791172+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/127887", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "968": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 968, - "name": "Generating coherent patterns of activity from chaotic neural networks (Sussillo and Abbott 2009)", - "repository_type": "github", - "summary": "\"Neural circuits display complex activity patterns both\r\nspontaneously and when responding to a stimulus or\r\ngenerating a motor output.\r\n\r\nHow are these two forms\r\nof activity related? \r\n\r\nWe develop a procedure called\r\nFORCE learning for modifying synaptic strengths\r\neither external to or within a model neural network\r\nto change chaotic spontaneous activity into a wide\r\nvariety of desired activity patterns.\r\n...\r\n Our results\r\nreproduce data on premovement activity in motor\r\nand premotor cortex, and suggest that synaptic plasticity\r\nmay be a more rapid and powerful modulator of\r\nnetwork activity than generally appreciated.\"", - "tags": [ - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1449, - "tag": "ModelDB:127967" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 791, - "tag": "Rate-coding model neurons" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - } - ], - "timestamp_created": "2024-01-12 09:34:45.396852+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/127967", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "969": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 969, - "name": "Accurate and fast simulation of channel noise in conductance-based model neurons (Linaro et al 2011)", - "repository_type": "github", - "summary": "We introduce and operatively present a general method to simulate channel noise in conductance-based model neurons, with modest computational overheads.\r\n\r\nOur approach may be considered as an accurate generalization of previous proposal methods,\r\nto the case of voltage-, ion-, and ligand-gated channels with arbitrary complexity.\r\n\r\nWe focus on the discrete Markov process descriptions, routinely employed in experimental\r\nidentification of voltage-gated channels and synaptic receptors.", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 748, - "tag": "Markov-type model" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1450, - "tag": "ModelDB:127992" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 569, - "tag": "Simplified Models" - } - ], - "timestamp_created": "2024-01-12 09:34:45.994236+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/127992", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "970": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 970, - "name": "Olfactory bulb cluster formation (Migliore et al. 2010)", - "repository_type": "github", - "summary": "Functional roles of distributed synaptic clusters in the mitral-granule cell network of the olfactory bulb.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1451, - "tag": "ModelDB:127995" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - } - ], - "timestamp_created": "2024-01-12 09:34:46.494562+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/127995", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "971": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 971, - "name": "Rapid desynchronization of an electrically coupled Golgi cell network (Vervaeke et al. 2010)", - "repository_type": "github", - "summary": "Electrical synapses between interneurons contribute to synchronized firing and network oscillations in the brain. However, little is known about how such networks respond to excitatory synaptic input. In addition to detailed electrophysiological recordings and histological investigations of electrically coupled Golgi cells in the cerebellum, a detailed network model of these cells was created. The cell models are based on reconstructed Golgi cell morphologies and the active conductances are taken from an earlier abstract Golgi cell model (Solinas et al 2007, accession no. 112685). Our results show that gap junction coupling can sometimes be inhibitory and either promote network synchronization or trigger rapid network desynchronization depending on the synaptic input. The model is available as a neuroConstruct project and can executable scripts can be generated for the NEURON simulator.", - "tags": [ - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 1433, - "tag": "I_AHP" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1452, - "tag": "ModelDB:127996" - }, - { - "id": 848, - "tag": "NeuroML (web link to model)" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 773, - "tag": "neuroConstruct (web link to model)" - } - ], - "timestamp_created": "2024-01-12 09:34:47.003699+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/127996", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "972": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 972, - "name": "Stochastic ion channels and neuronal morphology (Cannon et al. 2010)", - "repository_type": "github", - "summary": "\"... We introduce and\r\nvalidate new computational tools that enable efficient generation and simulation of models containing stochastic ion\r\nchannels distributed across dendritic and axonal membranes.\r\n\r\nComparison of five morphologically distinct neuronal cell\r\ntypes reveals that when all simulated neurons contain identical densities of stochastic ion channels, the amplitude of\r\nstochastic membrane potential fluctuations differs between cell types and depends on sub-cellular location.\r\n\r\n...\" The code is downloadable and more information is available at http://www.psics.org/", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1453, - "tag": "ModelDB:128043" - }, - { - "id": 1454, - "tag": "PSICS" - } - ], - "timestamp_created": "2024-01-12 09:34:47.628412+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/128043", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "973": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 973, - "name": "Generic Bi-directional Real-time Neural Interface (Zrenner et al. 2010)", - "repository_type": "github", - "summary": "Matlab/Simulink toolkit for generic multi-channel short-latency bi-directional neural-computer interactions. High-bandwidth (> 10 megabit per second) neural recording data can be analyzed in real-time while simultaneously generating specific complex electrical stimulation feedback with deterministically timed responses at sub-millisecond resolution. The commercially available 60-channel extracellular multi-electrode recording and stimulation set-up (Multichannelsystems GmbH MEA60) is used as an example hardware implementation.", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1455, - "tag": "ModelDB:128068" - }, - { - "id": 824, - "tag": "Simulink" - } - ], - "timestamp_created": "2024-01-12 09:34:48.136798+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/128068", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "974": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 974, - "name": "Fast sodium channel gating in mossy fiber axons (Schmidt-Hieber et al. 2010)", - "repository_type": "github", - "summary": "\"... To study the mechanisms underlying AP initiation in unmyelinated hippocampal mossy fibers of adult mice, we recorded sodium currents in axonal and somatic membrane patches. \r\n\r\nWe demonstrate that sodium channel density in the proximal axon is ~5 times higher than in the soma. \r\n\r\nFurthermore, sodium channel activation and inactivation are ~2 times faster. \r\n\r\nModeling revealed that the fast activation localized the initiation site to the proximal axon even upon strong synaptic stimulation, while fast inactivation contributed to energy-efficient membrane charging during APs. ...\"", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1456, - "tag": "ModelDB:128079" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:34:48.638274+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/128079", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "975": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 975, - "name": "Short term plasticity at the cerebellar granule cell (Nieus et al. 2006)", - "repository_type": "github", - "summary": "The model reproduces short term plasticity of the mossy fibre to granule cell synapse. To reproduce synaptic currents recorded in experiments, a model of presynaptic release was used to determine the concentration of glutamate in the synaptic cleft that ultimately determined a synaptic response. The parameters of facilitation and depression were determined deconvolving AMPA EPSCs.", - "tags": [ - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1457, - "tag": "ModelDB:128446" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - } - ], - "timestamp_created": "2024-01-12 09:34:49.139072+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/128446", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "976": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 976, - "name": "Leaky integrate-and-fire model of spike frequency adaptation in the LGMD (Gabbiani and Krapp 2006)", - "repository_type": "github", - "summary": "This will reproduce Figure 9 of Gabbiani and Krapp (2006) J Neurophysiol 96:2951-2962.\r\n\r\nThe figure simply shows that a leaky-integrate-and-fire model cannot reproduce spike frequency adaptation as it is seen experimentally in \r\nthe LGMD neuron.\r\n", - "tags": [ - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1458, - "tag": "ModelDB:128449" - }, - { - "id": 777, - "tag": "Spike Frequency Adaptation" - } - ], - "timestamp_created": "2024-01-12 09:34:49.643483+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/128449", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "977": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 977, - "name": "On stochastic diff. eq. models for ion channel noise in Hodgkin-Huxley neurons (Goldwyn et al. 2010)", - "repository_type": "github", - "summary": "\" ... We analyze three SDE models that have been proposed as approximations to the Markov chain model: one that describes the states of the ion channels and two that describe the states of the ion channel subunits. We show that the former channel-based approach can capture the distribution of channel noise and its effect on spiking in a Hodgkin-Huxley neuron model to a degree not previously demonstrated, but the latter two subunit-based approaches cannot. ...\"", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 710, - "tag": "FORTRAN" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1459, - "tag": "ModelDB:128502" - }, - { - "id": 874, - "tag": "Noise Sensitivity" - } - ], - "timestamp_created": "2024-01-12 09:34:50.161969+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/128502", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "978": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 978, - "name": "Mathematical model for windup (Aguiar et al. 2010)", - "repository_type": "github", - "summary": "\"Windup is characterized as a frequency-dependent\r\nincrease in the number of evoked action potentials in dorsal\r\nhorn neurons in response to electrical stimulation of afferent C-fibers.\r\n... \r\nThe approach presented here relies on mathematical and\r\ncomputational analysis to study the mechanism(s) underlying windup.\r\n\r\nFrom experimentally obtained windup profiles, we extract the time\r\nscale of the facilitation mechanisms that may support the characteristics\r\nof windup.\r\n\r\nGuided by these values and using simulations of a\r\nbiologically realistic compartmental model of a wide dynamic range\r\n(WDR) neuron, we are able to assess the contribution of each\r\nmechanism for the generation of action potentials windup.\r\n...\"", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1460, - "tag": "ModelDB:128559" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:34:50.700989+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/128559", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "979": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 979, - "name": "Grid cell oscillatory interference with noisy network oscillators (Zilli and Hasselmo 2010)", - "repository_type": "github", - "summary": "To examine whether an oscillatory interference model of grid cell activity could work if the oscillators were noisy neurons, we implemented these simulations. Here the oscillators are networks (either synaptically- or gap-junction--coupled) of one or more noisy neurons (either Izhikevich's simple model or a Hodgkin-Huxley--type biophysical model) which drive a postsynaptic cell (which may be integrate-and-fire, resonate-and-fire, or the simple model) which should fire spatially as a grid cell if the simulation is successful.", - "tags": [ - { - "id": 1461, - "tag": "Grid cell" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1462, - "tag": "ModelDB:128812" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 777, - "tag": "Spike Frequency Adaptation" - }, - { - "id": 586, - "tag": "Synchronization" - } - ], - "timestamp_created": "2024-01-12 09:34:51.218995+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/128812", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "980": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 980, - "name": "Medial reticular formation of the brainstem: anatomy and dynamics (Humphries et al. 2006, 2007)", - "repository_type": "github", - "summary": "A set of models to study the medial reticular formation (mRF) of the brainstem. We developed a collection of algorithms to derive the adult-state wiring of the model: one set a stochastic model; the other set mimicking the developmental process. We found that the anatomical models had small-world properties, irrespective of the choice of algorithm; and that the cluster-like organisation of the mRF may have arisen to minimise wiring costs. (The model code includes options to be run as dynamic models; papers examining these dynamics are included in the .zip file).", - "tags": [ - { - "id": 771, - "tag": "Action Selection/Decision Making" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 855, - "tag": "Connectivity matrix" - }, - { - "id": 793, - "tag": "Development" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1463, - "tag": "ModelDB:128816" - }, - { - "id": 758, - "tag": "Sensory processing" - } - ], - "timestamp_created": "2024-01-12 09:34:51.781229+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/128816", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "981": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 981, - "name": "Dopamine-modulated medium spiny neuron, reduced model (Humphries et al. 2009)", - "repository_type": "github", - "summary": "We extended Izhikevich's reduced model of the striatal medium spiny neuron (MSN) to account for dopaminergic modulation of its intrinsic ion channels and synaptic inputs. We tuned our D1 and D2 receptor MSN models using data from a recent (Moyer et al, 2007) large-scale compartmental model. Our new models capture the input-output relationships for both current injection and spiking input with remarkable accuracy, despite the order of magnitude decrease in system size. They also capture the paired pulse facilitation shown by MSNs. Our dopamine models predict that synaptic effects dominate intrinsic effects for all levels of D1 and D2 receptor activation. Our analytical work on these models predicts that the MSN is never bistable. Nonetheless, these MSN models can produce a spontaneously bimodal membrane potential similar to that recently observed in vitro following application of NMDA agonists. We demonstrate that this bimodality is created by modelling the agonist effects as slow, irregular and massive jumps in NMDA conductance and, rather than a form of bistability, is due to the voltage-dependent blockade of NMDA receptors", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 804, - "tag": "Bifurcation" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1464, - "tag": "ModelDB:128818" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 569, - "tag": "Simplified Models" - } - ], - "timestamp_created": "2024-01-12 09:34:52.294575+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/128818", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "982": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 982, - "name": "Striatal GABAergic microcircuit, dopamine-modulated cell assemblies (Humphries et al. 2009)", - "repository_type": "github", - "summary": "To begin identifying potential dynamically-defined computational elements within the striatum, we constructed a new three-dimensional model of the striatal microcircuit's connectivity, and instantiated this with our dopamine-modulated neuron models of the MSNs and FSIs. A new model of gap junctions between the FSIs was introduced and tuned to experimental data. We introduced a novel multiple spike-train analysis method, and apply this to the outputs of the model to find groups of synchronised neurons at multiple time-scales. We found that, with realistic in vivo background input, small assemblies of synchronised MSNs spontaneously appeared, consistent with experimental observations, and that the number of assemblies and the time-scale of synchronisation was strongly dependent on the simulated concentration of dopamine. We also showed that feed-forward inhibition from the FSIs counter-intuitively increases the firing rate of the MSNs.", - "tags": [ - { - "id": 771, - "tag": "Action Selection/Decision Making" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 855, - "tag": "Connectivity matrix" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1465, - "tag": "ModelDB:128874" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - } - ], - "timestamp_created": "2024-01-12 09:34:52.810398+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/128874", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "983": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 983, - "name": "Synthesis of spatial tuning functions from theta cell spike trains (Welday et al., 2011)", - "repository_type": "github", - "summary": "A single compartment model reproduces the firing rate maps of place, grid, and boundary cells by receiving inhibitory inputs from theta cells. The theta cell spike trains are modulated by the rat's movement velocity in such a way that phase interference among their burst pattern creates spatial envelope function which simulate the firing rate maps.", - "tags": [ - { - "id": 1466, - "tag": "Envelope synthesis" - }, - { - "id": 1461, - "tag": "Grid cell" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1467, - "tag": "ModelDB:129067" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 799, - "tag": "Place cell/field" - }, - { - "id": 586, - "tag": "Synchronization" - } - ], - "timestamp_created": "2024-01-12 09:34:53.365421+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/129067", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "984": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 984, - "name": "Continuous time stochastic model for neurite branching (van Elburg 2011)", - "repository_type": "github", - "summary": "\"In this paper we introduce a continuous time stochastic neurite branching model closely related to the discrete time stochastic BES-model. The discrete time BES-model is underlying current attempts to simulate cortical development, but is difficult to analyze. The new continuous time formulation facilitates analytical treatment thus allowing us to examine the structure of the model more closely. ...\"", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 793, - "tag": "Development" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1468, - "tag": "ModelDB:129071" - } - ], - "timestamp_created": "2024-01-12 09:34:53.859777+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/129071", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "985": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 985, - "name": "Increased computational accuracy in multi-compartmental cable models (Lindsay et al. 2005)", - "repository_type": "github", - "summary": "Compartmental models of dendrites are the most widely used tool for investigating their electrical\r\nbehaviour. \r\n\r\nTraditional models assign a single potential to a compartment. \r\n\r\nThis potential is associated with the membrane potential at the centre of the segment represented by the compartment.\r\n\r\nAll input to that segment, independent of its location on the segment, is assumed to act at the centre of the segment with the potential of the\r\ncompartment. \r\n\r\nBy contrast, the compartmental model introduced in this article assigns a potential to each end of a\r\nsegment, and takes into account the location of input to a segment on the model solution by partitioning the effect of\r\nthis input between the axial currents at the proximal and distal boundaries of segments.\r\n\r\nFor a given neuron, the new and traditional approaches to compartmental modelling use the same number of locations at which the membrane\r\npotential is to be determined, and lead to ordinary differential equations that are structurally identical. However, the\r\nsolution achieved by the new approach gives an order of magnitude better accuracy and precision than that achieved\r\nby the latter in the presence of point process input.", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1469, - "tag": "ModelDB:129149" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:34:54.385672+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/129149", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "986": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 986, - "name": "Intracortical synaptic potential modulation by presynaptic somatic potential (Shu et al. 2006, 2007)", - "repository_type": "github", - "summary": "\" ... Here we show that the voltage fluctuations associated with\r\ndendrosomatic synaptic activity propagate significant distances\r\nalong the axon, and that modest changes in the somatic membrane\r\npotential of the presynaptic neuron modulate the amplitude\r\nand duration of axonal action potentials and, through a Ca21-\r\ndependent mechanism, the average amplitude of the postsynaptic\r\npotential evoked by these spikes. \r\n\r\nThese results indicate that\r\nsynaptic activity in the dendrite and soma controls not only the\r\npattern of action potentials generated, but also the amplitude of\r\nthe synaptic potentials that these action potentials initiate in local\r\ncortical circuits, resulting in synaptic transmission that is a\r\nmixture of triggered and graded (analogue) signals.\"", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 1433, - "tag": "I_AHP" - }, - { - "id": 747, - "tag": "I_KD" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1470, - "tag": "ModelDB:135787" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 596, - "tag": "Synaptic Integration" - } - ], - "timestamp_created": "2024-01-12 09:34:55.296784+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/135787", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "987": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 987, - "name": "Action potential reconstitution from measured current waveforms (Alle et al. 2009)", - "repository_type": "github", - "summary": "This NEURON code reconstitutes action potentials in a model of a hippocampal mossy fiber from experimentally measured sodium, potassium and calcium current waveforms as described in Alle et al. (2009).\r\n", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1471, - "tag": "ModelDB:135838" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:34:55.865463+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/135838", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "988": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 988, - "name": "AP back-prop. explains threshold variability and rapid rise (McCormick et al. 2007, Yu et al. 2008)", - "repository_type": "github", - "summary": "This simple axon-soma model explained how the rapid rising phase in the somatic spike is derived from the propagated axon initiated spike,\r\nand how the somatic spike threshold variance is affected by spike propagation.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 1433, - "tag": "I_AHP" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1472, - "tag": "ModelDB:135839" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:34:56.402350+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/135839", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "989": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 989, - "name": "Selective control of cortical axonal spikes by a slowly inactivating K+ current (Shu et al. 2007)", - "repository_type": "github", - "summary": "We discovered a low-threshold, slowly inactivating K+ current, containing Kv1.2 alpha subunits, in axon initial segment, playing a key role in the modulation of spike threshold and spike duration as well as\r\nthe spike timing in prefrontal cortex layer V pyramidal cell of ferrets and rats.\r\nA kd.mod file implements this D current and put it in the axonal model: Neuron_Dcurrent.hoc. Run the model to see the gradual modulation effect over seconds on spike shape.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1473, - "tag": "ModelDB:135898" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:34:56.923358+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/135898", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "990": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 990, - "name": "High frequency oscillations in a hippocampal computational model (Stacey et al. 2009)", - "repository_type": "github", - "summary": "\"... Using a physiological computer model of hippocampus, we investigate random synaptic activity\r\n(noise) as a potential initiator of HFOs (high-frequency oscillations).\r\n\r\nWe explore parameters necessary to produce these oscillations and quantify the response\r\nusing the tools of stochastic resonance (SR) and coherence resonance\r\n(CR).\r\n... \r\n\r\nOur results show that, under normal coupling conditions, synaptic noise was able to produce\r\ngamma (30\u2013100 Hz) frequency oscillations.\r\n\r\n\r\nSynaptic noise generated HFOs in the ripple range (100\u2013200 Hz) when the network had\r\nparameters similar to pathological findings in epilepsy: increased gap\r\njunctions or recurrent synaptic connections, loss of inhibitory interneurons\r\nsuch as basket cells, and increased synaptic noise.\r\n\r\n...\r\nWe propose that increased synaptic noise and physiological coupling mechanisms are sufficient to generate gamma\r\noscillations and that pathologic changes in noise and coupling similar\r\nto those in epilepsy can produce abnormal ripples.\"\r\n", - "tags": [ - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1474, - "tag": "ModelDB:135902" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - } - ], - "timestamp_created": "2024-01-12 09:34:57.462345+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/135902", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "991": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 991, - "name": "Network recruitment to coherent oscillations in a hippocampal model (Stacey et al. 2011)", - "repository_type": "github", - "summary": "\"... Here we demonstrate, via a detailed computational model, a mechanism whereby physiological noise and coupling initiate oscillations and then recruit neighboring tissue, in a manner well described by a combination of Stochastic Resonance and Coherence Resonance.\r\n\r\n We develop a novel statistical method to quantify recruitment using several measures of network synchrony.\r\n\r\nThis measurement demonstrates that oscillations spread via preexisting network connections such as interneuronal connections, recurrent synapses, and gap junctions, provided that neighboring cells also receive sufficient inputs in the form of random synaptic noise.\r\n\r\n...\"\r\n", - "tags": [ - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1475, - "tag": "ModelDB:135903" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - } - ], - "timestamp_created": "2024-01-12 09:34:57.999944+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/135903", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "992": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 992, - "name": "Modelling enteric neuron populations and muscle fed-state motor patterns (Chambers et al. 2011)", - "repository_type": "github", - "summary": "\"After a meal, the gastrointestinal tract exhibits a set of behaviours known as the fed state.\r\n... Segmentation manifests as rhythmic local constrictions that do not propagate along the intestine.\r\n...\r\nWe investigated the enteric circuits that regulate segmentation focusing on a central feature of the ENS: a recurrent excitatory network of intrinsic sensory neurons (ISNs) which are characterized by prolonged after-hyperpolarizing potentials (AHPs) following their action potentials.\r\n...\"", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1476, - "tag": "ModelDB:136024" - }, - { - "id": 824, - "tag": "Simulink" - } - ], - "timestamp_created": "2024-01-12 09:34:58.509614+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/136024", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "993": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 993, - "name": "Functional structure of mitral cell dendritic tuft (Djurisic et al. 2008)", - "repository_type": "github", - "summary": "The computational modeling component of Djurisic et al. 2008 addressed two primary questions: whether amplification by active currents is necessary to explain the relatively mild attenuation suffered by tuft EPSPs spreading along the primary dendrite to the soma; what accounts for the relatively uniform peak EPSP amplitude throughout the tuft. These simulations show that passive spread from tuft to soma is sufficient to yield the low attenuation of tuft EPSPs, and that random distribution of a biologically plausible number of excitatory synapses throughout the tuft can produce the experimentally observed uniformity of depolarization.\r\n", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1477, - "tag": "ModelDB:136026" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 596, - "tag": "Synaptic Integration" - } - ], - "timestamp_created": "2024-01-12 09:34:59.112210+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/136026", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "994": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 994, - "name": "Synaptic information transfer in computer models of neocortical columns (Neymotin et al. 2010)", - "repository_type": "github", - "summary": "\"...\r\nWe sought to measure how the activity of the network alters information flow from inputs to output patterns. \r\nInformation handling by the network reflected the degree of internal connectivity. ...\r\nWith greater connectivity strength, the recurrent network translated activity and information due to contribution of activity from intrinsic network dynamics. \r\n...\r\nAt still higher internal synaptic strength, the network corrupted the external information, producing a state where little external information came through.\r\nThe association of increased information retrieved from the network with increased gamma power supports the notion of gamma oscillations playing a role in information processing.\"\r\n", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 1478, - "tag": "Information transfer" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1479, - "tag": "ModelDB:136095" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:34:59.714392+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/136095", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "995": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 995, - "name": "Multiscale model of olfactory receptor neuron in mouse (Dougherty 2009)", - "repository_type": "github", - "summary": "Collection of XPP (.ode) files simulating the signal transduction (slow) and action potential (fast) currents in the olfactory receptor neuron of mouse. Collection contains model configured for dual odorant pulse delivery and model configured for prolonged odorant delivery. For those interested more in transduction processes, each whole cell recording model comes with a counter part file configured to show just the slow transduction current for ease of use and convenience. These transduction-only models typically run faster than the full multi-scale models but do not demonstrate action potentials.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 781, - "tag": "G-protein coupled" - }, - { - "id": 1480, - "tag": "I ANO2" - }, - { - "id": 766, - "tag": "I CNG" - }, - { - "id": 780, - "tag": "I Cl,Ca" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 861, - "tag": "I_K,Na" - }, - { - "id": 817, - "tag": "I_Na,Ca" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1481, - "tag": "ModelDB:136097" - }, - { - "id": 866, - "tag": "Multiscale" - }, - { - "id": 738, - "tag": "Na/Ca exchanger" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 751, - "tag": "Signaling pathways" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 759, - "tag": "XPPAUT" - } - ], - "timestamp_created": "2024-01-12 09:35:00.391814+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/136097", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "996": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 996, - "name": "Cerebellar Nucleus Neuron (Steuber, Schultheiss, Silver, De Schutter & Jaeger, 2010)", - "repository_type": "github", - "summary": "This is the GENESIS 2.3 implementation of a multi-compartmental deep cerebellar nucleus (DCN) neuron model with a full dendritic morphology and appropriate active conductances. We generated a good match of our simulations with DCN current clamp data we recorded in acute slices, including the heterogeneity in the rebound responses. We then examined how inhibitory and excitatory synaptic input interacted with these intrinsic conductances to control DCN firing. We found that the output spiking of the model reflected the ongoing balance of excitatory and inhibitory input rates and that changing the level of inhibition performed an additive operation. Rebound firing following strong Purkinje cell input bursts was also possible, but only if the chloride reversal potential was more negative than -70 mV to allow de-inactivation of rebound currents. Fast rebound bursts due to T-type calcium current and slow rebounds due to persistent sodium current could be differentially regulated by synaptic input, and the pattern of these rebounds was further influenced by HCN current. Our findings suggest that active properties of DCN neurons could play a crucial role for signal processing in the cerebellum.", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 763, - "tag": "Intrinsic plasticity" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1482, - "tag": "ModelDB:136175" - }, - { - "id": 791, - "tag": "Rate-coding model neurons" - }, - { - "id": 573, - "tag": "Rebound firing" - }, - { - "id": 596, - "tag": "Synaptic Integration" - } - ], - "timestamp_created": "2024-01-12 09:35:01.063451+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/136175", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "997": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 997, - "name": "CA1 stratum radiatum interneuron multicompartmental model (Katona et al. 2011)", - "repository_type": "github", - "summary": "The model examines dendritic NMDA-spike generation and propagation\r\nin the dendrites of CA1 stratum radiatum interneurons. It contains\r\nNMDA-channels in a clustered pattern on a dendrite and K-channels. The\r\nsimulation shows the whole NMDA spike and the rising phase of the\r\ntraces in separate windows.\r\n", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1483, - "tag": "ModelDB:136176" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:35:01.594931+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/136176", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "998": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 998, - "name": "Myelinated nerve fibre myelin resistance dependent on extracellular K+ level (Brazhe et al. 2010)", - "repository_type": "github", - "summary": "Excitation leads to rise in paranodal [K]e under the myelin. This causes structural changes in myelin structure and resistance. Current model aims to simulate this aspect. This is a space-clamped model of a double-cable nerve fibre.", - "tags": [ - { - "id": 576, - "tag": "I K" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 783, - "tag": "I_Ks" - }, - { - "id": 1484, - "tag": "Intermittent block" - }, - { - "id": 1485, - "tag": "Lua" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1486, - "tag": "ModelDB:136296" - }, - { - "id": 740, - "tag": "Na/K pump" - } - ], - "timestamp_created": "2024-01-12 09:35:02.124075+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/136296", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "999": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 999, - "name": "Wang-Buzsaki Interneuron (Talathi et al., 2010)", - "repository_type": "github", - "summary": "The submitted code provides the relevant C++ files, matlabfiles and the data files essential to reproduce the figures in the JCNS paper titled Control of neural synchrony using channelrhodopsin-2: A computational study.", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1487, - "tag": "ModelDB:136308" - }, - { - "id": 586, - "tag": "Synchronization" - } - ], - "timestamp_created": "2024-01-12 09:35:02.642225+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/136308", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1000": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1000, - "name": "Neocort. pyramidal cells subthreshold somatic voltage controls spike propagation (Munro Kopell 2012)", - "repository_type": "github", - "summary": "There is suggestive evidence that pyramidal cell axons in neocortex may be coupled by gap junctions into an ``axonal plexus\" capable of generating Very Fast Oscillations (VFOs) with frequencies exceeding 80 Hz. It is not obvious, however, how a pyramidal cell in such a network could control its output when action potentials are free to propagate from the axons of other pyramidal cells into its own axon. We address this problem by means of simulations based on 3D reconstructions of pyramidal cells from rat somatosensory cortex. We show that somatic depolarization enables propagation via gap junctions into the initial segment and main axon, while somatic hyperpolarization disables it. We show further that somatic voltage cannot effectively control action potential propagation through gap junctions on minor collaterals; action potentials may therefore propagate freely from such collaterals regardless of somatic voltage. In previous work, VFOs are all but abolished during the hyperpolarization phase of slow-oscillations induced by anesthesia in vivo. This finding constrains the density of gap junctions on collaterals in our model and suggests that axonal sprouting due to cortical lesions may result in abnormally high gap junction density on collaterals, leading in turn to excessive VFO activity and hence to epilepsy via kindling.", - "tags": [ - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1488, - "tag": "ModelDB:136309" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - } - ], - "timestamp_created": "2024-01-12 09:35:03.178337+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/136309", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1001": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1001, - "name": "Modeling local field potentials (Bedard et al. 2004)", - "repository_type": "github", - "summary": "This demo simulates a model of local field potentials (LFP) with\r\nvariable resistivity. This model reproduces the low-pass\r\nfrequency filtering properties of extracellular potentials. The\r\nmodel considers inhomogeneous spatial profiles of conductivity\r\nand permittivity, which result from the multiple media (fluids,\r\nmembranes, vessels, ...) composing the extracellular space around\r\nneurons. Including non-constant profiles of conductivity enables\r\nthe model to display frequency filtering properties, ie slow\r\nevents such as EPSPs/IPSPs are less attenuated than fast events\r\nsuch as action potentials. The demo simulates Fig 6 of the\r\npaper.", - "tags": [ - { - "id": 734, - "tag": "Extracellular Fields" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1489, - "tag": "ModelDB:136310" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:35:03.677220+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/136310", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1002": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1002, - "name": "Globus pallidus neuron models with differing dendritic Na channel expression (Edgerton et al., 2010)", - "repository_type": "github", - "summary": "A set of 9 multi-compartmental rat GP neuron models (585 compartments) differing only in their expression of dendritic fast sodium channels were compared in their synaptic integration properties. Dendritic fast sodium channels were found to increase the importance of distal synapses (both excitatory AND inhibitory), increase spike timing variability with in vivo-like synaptic input, and make the model neurons highly sensitive to clustered synchronous excitation.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 595, - "tag": "Coincidence Detection" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1490, - "tag": "ModelDB:136315" - }, - { - "id": 596, - "tag": "Synaptic Integration" - } - ], - "timestamp_created": "2024-01-12 09:35:04.188063+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/136315", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1003": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1003, - "name": "Neural transformations on spike timing information (Tripp and Eliasmith 2007)", - "repository_type": "github", - "summary": "\" ... Here we employ computational methods to show that an ensemble\r\nof neurons firing at a constant mean rate can induce arbitrarily\r\nchosen temporal current patterns in postsynaptic cells. ...\"\r\n", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1491, - "tag": "ModelDB:136380" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - } - ], - "timestamp_created": "2024-01-12 09:35:05.229821+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/136380", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1004": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1004, - "name": "Action potential-evoked Na+ influx are similar in axon and soma (Fleidervish et al. 2010)", - "repository_type": "github", - "summary": "\"In cortical pyramidal neurons, the axon initial segment (AIS) is pivotal in synaptic integration.\r\n\r\nIt has been asserted that this is because there is a high density of Na+ channels in the AIS. \r\n\r\nHowever, we found that action potential-associated Na+ flux, as measured by high-speed fluorescence Na+ imaging, was about threefold larger in the rat AIS than in the soma. \r\n\r\nSpike-evoked Na+ flux in the AIS and the first node of Ranvier was similar and was eightfold lower in basal dendrites.\r\n\r\n...\r\nIn computer simulations, these data were consistent with the known features of action potential generation in these neurons.\"", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1492, - "tag": "ModelDB:136715" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 569, - "tag": "Simplified Models" - } - ], - "timestamp_created": "2024-01-12 09:35:05.847036+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/136715", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1005": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1005, - "name": "STDP allows fast rate-modulated coding with Poisson-like spike trains (Gilson et al. 2011)", - "repository_type": "github", - "summary": "The model demonstrates that a neuron equipped with STDP robustly detects repeating rate patterns among its afferents, from which the spikes are generated on the fly using inhomogenous Poisson sampling, provided those rates have narrow temporal peaks (10-20ms) - a condition met by many experimental Post-Stimulus Time Histograms (PSTH).", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 645, - "tag": "Brian" - }, - { - "id": 595, - "tag": "Coincidence Detection" - }, - { - "id": 1478, - "tag": "Information transfer" - }, - { - "id": 825, - "tag": "Learning" - }, - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1493, - "tag": "ModelDB:136717" - }, - { - "id": 874, - "tag": "Noise Sensitivity" - }, - { - "id": 719, - "tag": "Pattern Recognition" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 802, - "tag": "STDP" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 803, - "tag": "Unsupervised Learning" - } - ], - "timestamp_created": "2024-01-12 09:35:06.420848+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/136717", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1006": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1006, - "name": "Single neuron with dynamic ion concentrations (Cressman et al. 2009)", - "repository_type": "github", - "summary": "These are the full and reduced models of a generic single neuron with dynamic ion concentrations as described in Cressman et al., Journal of Computational Neuroscience (2009) 26:159\u2013170.", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1494, - "tag": "ModelDB:136773" - }, - { - "id": 759, - "tag": "XPPAUT" - } - ], - "timestamp_created": "2024-01-12 09:35:06.943713+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/136773", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1007": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1007, - "name": "Population models of temporal differentiation (Tripp and Eliasmith 2010)", - "repository_type": "github", - "summary": "\"Temporal derivatives are computed by a wide variety of neural circuits, but the problem of performing this computation accurately has received little theoretical study. \r\n\r\nHere we systematically compare the performance of diverse networks that calculate derivatives using cell-intrinsic adaptation and synaptic depression dynamics, feedforward network dynamics, and recurrent network dynamics. \r\n\r\nExamples of each type of network are compared by quantifying the errors they introduce into the calculation and their rejection of high-frequency input noise. \r\n...\"", - "tags": [ - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1495, - "tag": "ModelDB:136799" - }, - { - "id": 678, - "tag": "Nengo" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - } - ], - "timestamp_created": "2024-01-12 09:35:07.461344+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/136799", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1008": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1008, - "name": "Biophysically realistic neural modeling of the MEG mu rhythm (Jones et al. 2009)", - "repository_type": "github", - "summary": "\"Variations in cortical oscillations in the alpha (7\u201314 Hz) and beta (15\u201329 Hz) range have been correlated with attention, working memory, and stimulus detection. The mu rhythm recorded with magnetoencephalography (MEG) is a prominent oscillation generated by Rolandic cortex containing alpha and beta bands. Despite its prominence, the neural mechanisms regulating mu are unknown. We characterized the ongoing MEG mu rhythm from a localized source in the finger representation of primary somatosensory (SI) cortex. Subjects showed variation in the relative expression of mu-alpha or mu-beta, which were nonoverlapping for roughly 50% of their respective durations on single trials. To delineate the origins of this rhythm, a biophysically principled computational neural model of SI was developed, with distinct laminae, inhibitory and excitatory neurons, and feedforward (FF, representative of lemniscal thalamic drive) and feedback (FB, representative of higher-order cortical drive or input from nonlemniscal thalamic nuclei) inputs defined by the laminar location of their postsynaptic effects. ...\"", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1496, - "tag": "ModelDB:136803" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 764, - "tag": "Touch" - } - ], - "timestamp_created": "2024-01-12 09:35:08.004856+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/136803", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1009": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1009, - "name": "Democratic population decisions result in robust policy-gradient learning (Richmond et al. 2011)", - "repository_type": "github", - "summary": "This model demonstrates the use of GPU programming (with CUDA) to simulate a two-layer network of Integrate-and-Fire neurons with varying degrees of recurrent connectivity and to investigate its ability to learn a simplified navigation task using a learning rule stemming from Reinforcement Learning, a policy-gradient rule.", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 825, - "tag": "Learning" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1497, - "tag": "ModelDB:136807" - }, - { - "id": 822, - "tag": "Winner-take-all" - } - ], - "timestamp_created": "2024-01-12 09:35:08.536087+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/136807", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1010": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1010, - "name": "Parameter estimation for Hodgkin-Huxley based models of cortical neurons (Lepora et al. 2011)", - "repository_type": "github", - "summary": "Simulation and fitting of two-compartment (active soma, passive dendrite) \r\nfor different classes of cortical neurons. The fitting technique \r\nindirectly matches neuronal currents derived from somatic membrane \r\npotential data rather than fitting the voltage traces directly. \r\nThe method uses an analytic solution for the somatic ion channel \r\nmaximal conductances given approximate models of the channel kinetics, \r\nmembrane dynamics and dendrite. This approach is tested on model-derived \r\ndata for various cortical neurons. \r\n", - "tags": [ - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1498, - "tag": "ModelDB:136808" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 863, - "tag": "Parameter sensitivity" - }, - { - "id": 569, - "tag": "Simplified Models" - } - ], - "timestamp_created": "2024-01-12 09:35:09.091935+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/136808", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1011": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1011, - "name": "A model of unitary responses from A/C and PP synapses in CA3 pyramidal cells (Baker et al. 2010)", - "repository_type": "github", - "summary": "The model was used to reproduce experimentally determined mean synaptic response characteristics of unitary AMPA and NMDA synaptic stimulations in CA3 pyramidal cells with the objective of inferring the most likely response properties of the corresponding types of synapses. The model is primarily concerned with passive cells, but models of active dendrites are included.", - "tags": [ - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1499, - "tag": "ModelDB:137259" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:35:09.724964+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/137259", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1012": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1012, - "name": "Effect of trp-like current on APs during exposure to sinusoidal voltage (Chen et al. 2010)", - "repository_type": "github", - "summary": "\"...\r\nPrevious work showed that magnetic electrical field-induced antinoceptive action is mediated by activation of capsaicin-sensitive sensory afferents. In this study, a modified Hodgkin-Huxley model, in which TRP-like current (I-TRP) was incorporated, was implemented to predict the firing behavior of action potentials (APs), as the model neuron was exposed to sinusoidal changes in externally-applied voltage. \r\n\r\n...\r\nOur simulation results suggest that modulation of TRP-like channels functionally expressed in small-diameter peripheral sensory neurons should be an important mechanism through which it can contribute to the firing pattern of APs.\"", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 1500, - "tag": "I trp" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1501, - "tag": "ModelDB:137263" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 732, - "tag": "Tutorial/Teaching" - }, - { - "id": 759, - "tag": "XPPAUT" - } - ], - "timestamp_created": "2024-01-12 09:35:10.337127+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/137263", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1013": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1013, - "name": "Striatal GABAergic microcircuit, spatial scales of dynamics (Humphries et al, 2010)", - "repository_type": "github", - "summary": "The main thrust of this paper was the development of the 3D anatomical network of the striatum's GABAergic microcircuit. We grew dendrite and axon models for the MSNs and FSIs and extracted probabilities for the presence of these neurites as a function of distance from the soma. From these, we found the probabilities of intersection between the neurites of two neurons given their inter-somatic distance, and used these to construct three-dimensional striatal networks. These networks were examined for their predictions for the distributions of the numbers and distances of connections for all the connections in the microcircuit. We then combined the neuron models from a previous model (Humphries et al, 2009; ModelDB ID: 128874) with the new anatomical model. We used this new complete striatal model to examine the impact of the anatomical network on the firing properties of the MSN and FSI populations, and to study the influence of all the inputs to one MSN within the network.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 855, - "tag": "Connectivity matrix" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1502, - "tag": "ModelDB:137502" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 822, - "tag": "Winner-take-all" - } - ], - "timestamp_created": "2024-01-12 09:35:10.866522+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/137502", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1014": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1014, - "name": "Model of long range transmission of gamma oscillation (Murray 2007)", - "repository_type": "github", - "summary": "\"...\r\nA minimal mathematical model was developed for a\r\npreliminary study of long-range neural transmission of gamma\r\noscillation from the CA3 to the entorhinal cortex via the CAI\r\nregion of the hippocampus, a subset within a larger complex set of\r\npathways. A module was created for each local population of\r\nneurons with common intrinsic properties and connectivity to\r\nsimplify the connection process and make the model more flexible.\r\nThree modules were created using MATLAB Simulink\u00ae and tested\r\nto confirm that they transmit gamma through the system. The\r\nmodel also revealed that a portion of the signal from CAI to the\r\nentorhinal cortex may be lost in transmission under certain\r\nconditions.\"", - "tags": [ - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1503, - "tag": "ModelDB:137505" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 569, - "tag": "Simplified Models" - } - ], - "timestamp_created": "2024-01-12 09:35:11.387996+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/137505", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1015": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1015, - "name": "Sensory feedback in an oscillatory interference model of place cell activity (Monaco et al. 2011)", - "repository_type": "github", - "summary": "Many animals use a form of dead reckoning known as 'path integration' to maintain a sense of their location as they explore the world. However, internal motion signals and the neural activity that integrates them can be noisy, leading inevitably to inaccurate position estimates. The rat hippocampus and entorhinal cortex support a flexible system of spatial representation that is critical to spatial learning and memory. The position signal encoded by this system is thought to rely on path integration, but it must be recalibrated by familiar landmarks to maintain accuracy. To explore the interaction between path integration and external landmarks, we present a model of hippocampal activity based on the interference of theta-frequency oscillations that are modulated by realistic animal movements around a track. We show that spatial activity degrades with noise, but introducing external cues based on direct sensory feedback can prevent this degradation. When these cues are put into conflict with each other, their interaction produces a diverse array of response changes that resembles experimental observations. Feedback driven by attending to landmarks may be critical to navigation and spatial memory in mammals.", - "tags": [ - { - "id": 1466, - "tag": "Envelope synthesis" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1504, - "tag": "ModelDB:137676" - }, - { - "id": 874, - "tag": "Noise Sensitivity" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 1505, - "tag": "Phase interference" - }, - { - "id": 799, - "tag": "Place cell/field" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 791, - "tag": "Rate-coding model neurons" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 586, - "tag": "Synchronization" - } - ], - "timestamp_created": "2024-01-12 09:35:12.065085+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/137676", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1016": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1016, - "name": "Laminar analysis of excitatory circuits in vibrissal motor and sensory cortex (Hooks et al. 2011)", - "repository_type": "github", - "summary": "\"...\r\nWe mapped local excitatory pathways in each area (primary motor cortex (vM1), primary somatosensory cortex (vS1;\r\nbarrel cortex), and secondary somatosensory cortex (S2)) across all cortical layers using glutamate uncaging and laser scanning photostimulation. \r\n\r\nWe analyzed these maps to derive laminar connectivity matrices describing the average strengths of pathways between individual neurons in\r\ndifferent layers and between entire cortical layers. ...\"", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 855, - "tag": "Connectivity matrix" - }, - { - "id": 856, - "tag": "Laminar Connectivity" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1506, - "tag": "ModelDB:137743" - }, - { - "id": 764, - "tag": "Touch" - }, - { - "id": 1507, - "tag": "Whisking" - } - ], - "timestamp_created": "2024-01-12 09:35:12.747464+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/137743", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1017": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1017, - "name": "CA1 pyramidal: Stochastic amplification of KCa in Ca2+ microdomains (Stanley et al. 2011)", - "repository_type": "github", - "summary": "This minimal model investigates stochastic amplification of calcium-activated potassium (KCa) currents. Amplification results from calcium being released in short high amplitude pulses associated with the stochastic gating of calcium channels in microdomains. This model predicts that such pulsed release of calcium significantly increases subthreshold SK2 currents above what would be produced by standard deterministic models. However, there is little effect on a simple sAHP current kinetic scheme. This suggests that calcium stochasticity and microdomains should be considered when modeling certain KCa currents near subthreshold conditions.", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 1433, - "tag": "I_AHP" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 748, - "tag": "Markov-type model" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1508, - "tag": "ModelDB:137745" - }, - { - "id": 874, - "tag": "Noise Sensitivity" - } - ], - "timestamp_created": "2024-01-12 09:35:13.269238+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/137745", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1018": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1018, - "name": "Spike exchange methods for a Blue Gene/P supercomputer (Hines et al., 2011)", - "repository_type": "github", - "summary": "Tests several spike exchange methods on a Blue Gene/P supercomputer on up to 64K cores.", - "tags": [ - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1509, - "tag": "ModelDB:137845" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:35:13.908061+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/137845", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1019": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1019, - "name": "GP Neuron, somatic and dendritic phase response curves (Schultheiss et al. 2011)", - "repository_type": "github", - "summary": "Phase response analysis of a GP neuron model showing type I PRCs for somatic inputs and type II PRCs for dendritic excitation. Analysis of intrinsic currents underlying type II dendritic PRCs.", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1510, - "tag": "ModelDB:137846" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 823, - "tag": "Phase Response Curves" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 586, - "tag": "Synchronization" - } - ], - "timestamp_created": "2024-01-12 09:35:14.704094+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/137846", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1020": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1020, - "name": "Vectorized algorithms for spiking neural network simulation (Brette and Goodman 2011)", - "repository_type": "github", - "summary": "\"... We describe a set of algorithms to simulate large spiking neural networks\r\nefficiently with high-level languages using vector-based operations.\r\nThese algorithms constitute the core of Brian, a spiking neural network\r\nsimulator written in the Python language. Vectorized simulation makes\r\nit possible to combine the flexibility of high-level languages with the\r\ncomputational efficiency usually associated with compiled languages.\"", - "tags": [ - { - "id": 1511, - "tag": "Brian (web link to method)" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1512, - "tag": "ModelDB:137989" - }, - { - "id": 853, - "tag": "Python (web link to model)" - } - ], - "timestamp_created": "2024-01-12 09:35:15.231214+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/137989", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1021": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1021, - "name": "HMM of Nav1.7 WT and F1449V (Gurkiewicz et al. 2011)", - "repository_type": "github", - "summary": "Neuron mod files for the WT and F1449V Na+ currents from the paper:\r\nKinetic Modeling of Nav1.7 Provides Insight Into Erythromelalgia-associated F1449V Mutation\r\nM. Gurkiewicz, A. Korngreen, S. Waxman, and A. Lampert. J.Neurophysiol. (2011).\r\n\r\nThe parameters for the K65, K53 and K63 transitions were derived from microscopic reversibility relationships in the model.", - "tags": [ - { - "id": 1513, - "tag": "Erythromelalgia" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1514, - "tag": "ModelDB:138082" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 775, - "tag": "Nociception" - } - ], - "timestamp_created": "2024-01-12 09:35:15.757577+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/138082", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1022": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1022, - "name": "CA1 pyramidal neuron: schizophrenic behavior (Migliore et al. 2011)", - "repository_type": "github", - "summary": "NEURON files from the paper: A modeling study suggesting how a reduction in the context-dependent input on CA1 pyramidal neurons could generate schizophrenic behavior. by M. Migliore, I. De Blasi, D. Tegolo, R. Migliore, Neural Networks,(2011), doi:10.1016/j.neunet.2011.01.001. Starting from the experimentally supported assumption on hippocampal neurons we explore an experimentally testable prediction at the single neuron level. The model shows how and to what extent a pathological hypofunction of a contextdependent distal input on a CA1 neuron can generate hallucinations by altering the normal recall of objects on which the neuron has been previously tuned. The results suggest that a change in the context during the recall phase may cause an occasional but very significant change in the set of active dendrites used for features recognition, leading to a distorted perception of objects.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 595, - "tag": "Coincidence Detection" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 1515, - "tag": "Hallucinations" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1516, - "tag": "ModelDB:138205" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 845, - "tag": "Schizophrenia" - }, - { - "id": 596, - "tag": "Synaptic Integration" - } - ], - "timestamp_created": "2024-01-12 09:35:16.279069+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/138205", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1023": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1023, - "name": "Stochastic 3D model of neonatal rat spinal motoneuron (Ostroumov 2007)", - "repository_type": "github", - "summary": "\" ... Although existing models of motoneurons have indicated the distributed role of certain conductances in\r\nregulating firing, it is unclear how the spatial distribution of certain currents is ultimately shaping motoneuron output. \r\n\r\nThus, it would be helpful to\r\nbuild a bridge between histological and electrophysiological data. \r\n\r\nThe present report is based on the construction of a 3D motoneuron model based\r\non available parameters applicable to the neonatal spinal cord. ...\"", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1517, - "tag": "ModelDB:138273" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:35:16.860525+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/138273", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1024": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1024, - "name": "Mechanisms of magnetic stimulation of central nervous system neurons (Pashut et al. 2011)", - "repository_type": "github", - "summary": "Transcranial magnetic stimulation (TMS) is a widely applied tool for probing cognitive function in humans and is one of the best tools for clinical treatments and interfering with cognitive tasks. Surprisingly, while TMS has been commercially available for decades, the cellular mechanisms underlying magnetic stimulation remain unclear. Here we investigate these mechanisms using compartmental modeling. We generated a numerical scheme allowing simulation of the physiological response to magnetic stimulation of neurons with arbitrary morphologies and active properties. Computational experiments using this scheme suggested that TMS affects neurons in the central nervous system (CNS) primarily by somatic stimulation.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 1518, - "tag": "Magnetic stimulation" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1519, - "tag": "ModelDB:138321" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:35:17.390665+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/138321", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1025": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1025, - "name": "Emergence of physiological oscillation frequencies in neocortex simulations (Neymotin et al. 2011)", - "repository_type": "github", - "summary": "\"Coordination of neocortical oscillations has been hypothesized to underlie the \"binding\" essential to cognitive function.\r\n\r\nHowever, the mechanisms that generate neocortical oscillations in physiological frequency bands remain unknown.\r\n\r\nWe hypothesized that interlaminar relations in neocortex would provide multiple intermediate loops that would play particular roles in generating oscillations, adding different dynamics to the network.\r\n\r\nWe simulated networks from sensory neocortex using 9 columns of event-driven rule-based neurons wired according to anatomical data and driven with random white-noise synaptic inputs.\r\n...\"", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 856, - "tag": "Laminar Connectivity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1520, - "tag": "ModelDB:138379" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 586, - "tag": "Synchronization" - } - ], - "timestamp_created": "2024-01-12 09:35:17.925238+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/138379", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1026": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1026, - "name": "Controlling KCa channels with different Ca2+ buffering models in Purkinje cell (Anwar et al. 2012)", - "repository_type": "github", - "summary": "In this work, we compare the dynamics of different buffering models during generation of a dendritic Ca2+ spike in a single compartment model of a Purkinje cell dendrite. The Ca2+ buffering models used are 1) a single Ca2+ pool, 2) two Ca2+ pools respectively for the fast and slow transients, 3) a detailed calcium model with buffers, pump (Schmidt et al., 2003), and diffusion and 4) a calcium model with buffers, pump and diffusion compensation. The parameters of single pool and double pool are tuned, using Neurofitter (Van Geit et al., 2007), to approximate the behavior of detailed calcium dynamics over range of 0.5 \u00c2\u00b5M to 8 \u00c2\u00b5M of intracellular calcium. The diffusion compensation is modeled using a buffer-like mechanism called DCM. To use DCM robustly for different diameter compartments, its parameters are estimated, using Neurofitter (Van Geit et al., 2007), as a function of compartment diameter (0.8 \u00c2\u00b5m-20 \u00c2\u00b5m).", - "tags": [ - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1521, - "tag": "ModelDB:138382" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:35:18.439438+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/138382", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1027": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 1027, - "name": "Gamma and theta rythms in biophysical models of hippocampus circuits (Kopell et al. 2011)", - "repository_type": "github", - "summary": "\" ... the main rhythms displayed by the hippocampus, the gamma (30\u201390 Hz) and theta (4\u201312 Hz) rhythms. We concentrate on modeling\r\nin vitro experiments, but with an eye toward possible in vivo implications. ...\r\nWe use simpler biophysical models; all cells have a single compartment only, and the\r\ninterneurons are restricted to two types: fast-spiking (FS) basket cells and oriens\r\nlacunosum-moleculare (O-LM) cells. \r\n... , we aim not so much at reproducing dynamics in great detail, but at clarifying the essential mechanisms underlying the production of the rhythms and their interactions (Kopell, 2005). ...\"\r\n", - "tags": [ - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1522, - "tag": "ModelDB:138421" - }, - { - "id": 585, - "tag": "Oscillations" - } - ], - "timestamp_created": "2024-01-12 09:35:18.976401+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/138421", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1028": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 1028, - "name": "Prefrontal\u2013striatal Parkinsons comp. model of multicue category learning (Moustafa and Gluck 2011)", - "repository_type": "github", - "summary": "\"...\r\nIn this\r\nmodel, PFC dopamine is key for attentional learning, whereas\r\nbasal ganglia dopamine, consistent with other models, is key for\r\nreinforcement and motor learning.\r\n\r\nThe model assumes that competitive\r\ndynamics among PFC neurons is the neural mechanism\r\nunderlying stimulus selection with limited attentional resources,\r\nwhereas competitive dynamics among striatal neurons is the\r\nneural mechanism underlying action selection. \r\n\r\nAccording to our\r\nmodel, PD is associated with decreased phasic and tonic dopamine\r\nlevels in both PFC and basal ganglia. \r\n...\"", - "tags": [ - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1523, - "tag": "ModelDB:138631" - }, - { - "id": 801, - "tag": "Parkinson's" - } - ], - "timestamp_created": "2024-01-12 09:35:19.569473+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/138631", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1029": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 1029, - "name": "A neurocomputational model of classical conditioning phenomena (Moustafa et al. 2009)", - "repository_type": "github", - "summary": "\"... Here, we show that the same information-processing function proposed\r\nfor the hippocampal region in the Gluck and Myers (1993) model can also be implemented in\r\na network without using the backpropagation algorithm. Instead, our newer instantiation of\r\nthe theory uses only (a) Hebbian learning methods which match more closely with synaptic\r\nand associative learning mechanisms ascribed to the hippocampal region and (b) a more\r\nplausible representation of input stimuli. \r\nWe demonstrate here that this new more\r\nbiologically plausible model is able to simulate various behavioral effects, including latent\r\ninhibition, acquired equivalence, sensory preconditioning, negative patterning, and context\r\nshift effects. \r\n...\"", - "tags": [ - { - "id": 825, - "tag": "Learning" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1524, - "tag": "ModelDB:138634" - }, - { - "id": 758, - "tag": "Sensory processing" - } - ], - "timestamp_created": "2024-01-12 09:35:20.058525+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/138634", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1030": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1030, - "name": "Stochastic versions of the Hodgkin-Huxley equations (Goldwyn, Shea-Brown 2011)", - "repository_type": "github", - "summary": "A Matlab gui for simulating different channel noise models using the Hodgkin-Huxley equations. Methods provided and reviewed in Goldwyn and Shea-Brown (2011) are: current noise, subunit noise, conductance noise, and Markov chain, as well as the standard deterministic Hodgkin-Huxley model.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 748, - "tag": "Markov-type model" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1525, - "tag": "ModelDB:138950" - } - ], - "timestamp_created": "2024-01-12 09:35:20.563411+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/138950", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1031": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1031, - "name": "Modular grid cell responses as a basis for hippocampal remapping (Monaco and Abbott 2011)", - "repository_type": "github", - "summary": "\"Hippocampal place fields, the local regions of activity recorded from place cells in exploring rodents, can undergo large changes in relative\r\nlocation during remapping.\r\n\r\nThis process would appear to require some form of modulated global input.\r\n\r\nGrid-cell responses recorded from layer II of medial entorhinal cortex in rats have been observed to realign concurrently with hippocampal remapping, making them a candidate input source.\r\n\r\nHowever, this realignment occurs coherently across colocalized ensembles of grid cells (Fyhn et al., 2007).\r\n\r\nThe hypothesized entorhinal contribution to remapping depends on whether this coherence extends to all grid cells, which is currently\r\nunknown.\r\n\r\nWe study whether dividing grid cells into small numbers of independently realigning modules can both account for this\r\nlocalized coherence and allow for hippocampal remapping.\r\n...\"", - "tags": [ - { - "id": 855, - "tag": "Connectivity matrix" - }, - { - "id": 1461, - "tag": "Grid cell" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1526, - "tag": "ModelDB:138951" - }, - { - "id": 719, - "tag": "Pattern Recognition" - }, - { - "id": 799, - "tag": "Place cell/field" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 791, - "tag": "Rate-coding model neurons" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - } - ], - "timestamp_created": "2024-01-12 09:35:21.086665+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/138951", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1032": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1032, - "name": "A Neural mass computational model of the Thalamocorticothalamic circuitry (Bhattacharya et al. 2011)", - "repository_type": "github", - "summary": "The model presented here is a bio-physically plausible version of a simple thalamo-cortical neural mass computational model proposed by Lopes da Silva in 1974 to simulate brain EEG activity within the alpha band (8-13 Hz). The thalamic and cortical circuitry are presented as separate modules in this model with cell populations as in biology. The connectivity between cell populations are as reported by Sherman, S. in Scholarpedia, 2006. The values of the synaptic connectivity parameters are as reported by Van Horn et al, 2000. In our paper (doi:10.1016/j.neunet.2011.02.009), we study the model behaviour while varying the values of the synaptic connectivity parameters (Cyyy) in the model about their respective 'basal' (intial) values.", - "tags": [ - { - "id": 827, - "tag": "Aging/Alzheimer`s" - }, - { - "id": 1527, - "tag": "Brain Rhythms" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1528, - "tag": "ModelDB:138970" - }, - { - "id": 824, - "tag": "Simulink" - } - ], - "timestamp_created": "2024-01-12 09:35:21.595559+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/138970", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1033": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1033, - "name": "Interacting synaptic conductances during, distorting, voltage clamp (Poleg-Polsky and Diamond 2011)", - "repository_type": "github", - "summary": "This simulation examines the accuracy of the voltage clamp technique\r\nin detecting the excitatory and the inhibitory components of the\r\nsynaptic drive.", - "tags": [ - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1529, - "tag": "ModelDB:139150" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 596, - "tag": "Synaptic Integration" - } - ], - "timestamp_created": "2024-01-12 09:35:22.132471+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/139150", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1034": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1034, - "name": "Single neuron with ion concentrations to model anoxic depolarization (Zandt et al. 2011)", - "repository_type": "github", - "summary": "A minimal single neuron model, including changing ion concentrations and homeostasis mechanisms. It shows the sudden depolarization that occurs after prolonged anoxia/ischemia.", - "tags": [ - { - "id": 1530, - "tag": "Anoxic depolarization" - }, - { - "id": 1531, - "tag": "I Cl, leak" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 1532, - "tag": "I Na, leak" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1533, - "tag": "ModelDB:139266" - }, - { - "id": 740, - "tag": "Na/K pump" - } - ], - "timestamp_created": "2024-01-12 09:35:22.643451+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/139266", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1035": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1035, - "name": "A spatially extended model for macroscopic spike-wave discharges (Taylor and Baier 2011)", - "repository_type": "github", - "summary": "A spatially extended neural field model for generating spike-wave based on the Amari (1977) model implemented in MATLAB.", - "tags": [ - { - "id": 1527, - "tag": "Brain Rhythms" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1534, - "tag": "ModelDB:139296" - } - ], - "timestamp_created": "2024-01-12 09:35:23.258097+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/139296", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1036": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1036, - "name": "Bursting respiratory net: clustered architecture gives large phase diff`s (Fietkiewicz et al 2011)", - "repository_type": "github", - "summary": "Using a previous model of respiratory rhythm generation, we modified the network architecture such that cells can be segregated into two clusters. Cells within a given cluster burst with smaller phase differences than do cells from different clusters. This may explain the large phase differences seen experimentally, as reported in the paper.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1535, - "tag": "ModelDB:139418" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1536, - "tag": "Respiratory control" - } - ], - "timestamp_created": "2024-01-12 09:35:23.757723+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/139418", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1037": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1037, - "name": "Ketamine disrupts theta modulation of gamma in a computer model of hippocampus (Neymotin et al 2011)", - "repository_type": "github", - "summary": "\"Abnormalities in oscillations have been suggested to play a role in schizophrenia. \r\n\r\nWe studied theta-modulated gamma oscillations in a computer model of hippocampal CA3 in vivo with and\r\nwithout simulated application of ketamine, an NMDA receptor antagonist and psychotomimetic.\r\n\r\nNetworks of 1200 multi-compartment neurons (pyramidal, basket and oriens-lacunosum moleculare,\r\nOLM, cells) generated theta and gamma oscillations from intrinsic network dynamics: basket cells\r\nprimarily generated gamma and amplified theta, while OLM cells strongly contributed to theta.\r\n...\"", - "tags": [ - { - "id": 1527, - "tag": "Brain Rhythms" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 1478, - "tag": "Information transfer" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1537, - "tag": "ModelDB:139421" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 787, - "tag": "Pathophysiology" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 845, - "tag": "Schizophrenia" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 746, - "tag": "Therapeutics" - } - ], - "timestamp_created": "2024-01-12 09:35:24.295379+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/139421", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1038": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 1038, - "name": "Mathematics for Neuroscientists (Gabbiani and Cox 2010)", - "repository_type": "github", - "summary": "This textbook provides a good source for learning the mathematics relevant to computational neuroscience and also the neuroscience itself. There are 232 computer code examples from the book available through the http://www.elsevierdirect.com/companions/9780123748829/pictures/code/index.html code link here and in the below page copied from the books companion web site.", - "tags": [ - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1538, - "tag": "ModelDB:139457" - }, - { - "id": 732, - "tag": "Tutorial/Teaching" - } - ], - "timestamp_created": "2024-01-12 09:35:24.958125+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/139457", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1039": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1039, - "name": "L5b PC model constrained for BAC firing and perisomatic current step firing (Hay et al., 2011)", - "repository_type": "github", - "summary": "\"...\r\nL5b pyramidal cells have been the subject of extensive experimental and modeling studies, yet conductance-based models of these cells that faithfully reproduce both their perisomatic Na+-spiking behavior as well as key dendritic active properties, including Ca2+ spikes and back-propagating action potentials, are still lacking.\r\n\r\nBased on a large body of experimental recordings from both the soma and dendrites of L5b pyramidal cells in adult rats, we characterized key features of the somatic and dendritic firing and quantified their statistics.\r\n\r\nWe used these features to constrain the density of a set of ion channels over the soma and dendritic surface via multi-objective optimization with an evolutionary algorithm, thus generating a set of detailed conductance-based models that faithfully replicate the back-propagating action potential activated Ca2+ spike firing and the perisomatic firing response to current steps, as well as the experimental variability of the properties.\r\n...\r\nThe models we present provide several experimentally-testable predictions and can serve as a powerful tool for theoretical investigations of the contribution of single-cell dynamics to network activity and its computational capabilities.\r\n\"", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1539, - "tag": "ModelDB:139653" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 568, - "tag": "Parameter Fitting" - } - ], - "timestamp_created": "2024-01-12 09:35:25.648455+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/139653", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1040": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1040, - "name": "Frog second-order vestibular neuron models (Rossert et al. 2011)", - "repository_type": "github", - "summary": "This implements spiking Hodgkin-Huxley type models of tonic and phasic second-order vestibular neurons. Models fitted to intracellular spike and membrane potential recordings from frog (Rana temporaria). The models can be stimulated by intracellular step current, frequency current (ZAP) or synaptic stimulation.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1540, - "tag": "ModelDB:139654" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 758, - "tag": "Sensory processing" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 753, - "tag": "Vestibular" - } - ], - "timestamp_created": "2024-01-12 09:35:26.215517+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/139654", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1041": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1041, - "name": "Hippocampus CA1: Simulations of LTP signaling pathways (Kim M et al. 2011)", - "repository_type": "github", - "summary": "This is a multi-compartmental, stochastic version of the Kim et al. 2010 paper. There are a few additional reactions, and some of the rate constants have been updated. It addresses the role of molecule anchoring in PKA dependent hippocampal LTP.\r\n", - "tags": [ - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1541, - "tag": "ModelDB:139655" - }, - { - "id": 1542, - "tag": "NeuroRD" - }, - { - "id": 751, - "tag": "Signaling pathways" - } - ], - "timestamp_created": "2024-01-12 09:35:26.724441+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/139655", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1042": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1042, - "name": "Cerebellar cortex oscil. robustness from Golgi cell gap jncs (Simoes de Souza and De Schutter 2011)", - "repository_type": "github", - "summary": "\" ... Previous one-dimensional network modeling of the cerebellar granular layer has been successfully\r\nlinked with a range of cerebellar cortex oscillations observed in vivo. However, the recent discovery of gap\r\njunctions between Golgi cells (GoCs), which may cause oscillations by themselves, has raised the question of how\r\ngap-junction coupling affects GoC and granular-layer oscillations. To investigate this question, we developed a\r\nnovel two-dimensional computational model of the GoC-granule cell (GC) circuit with and without gap junctions\r\nbetween GoCs. ...\"", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1543, - "tag": "ModelDB:139656" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 586, - "tag": "Synchronization" - } - ], - "timestamp_created": "2024-01-12 09:35:27.228263+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/139656", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1043": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1043, - "name": "Superior paraolivary nucleus neuron (Kopp-Scheinpflug et al. 2011)", - "repository_type": "github", - "summary": "This is a model of neurons in the brainstem superior paraolivary nucleus (SPN), which produce very salient offset firing during sound stimulation. Rebound offset firing is triggered by IPSPs coming from the medial nucleus of the trapezoid body (MNTB). This model shows that AP firing can emerge from inhibition through integration of large IPSPs, driven by an\r\nextremely negative chloride reversal potential, combined with a large hyperpolarization-\r\nactivated non-specific cationic current (IH), with a secondary contribution from a T-type calcium conductance (ITCa). As a result, tiny gaps in sound stimuli of just 3-4ms can elicit reliable APs that signal such brief offsets.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1544, - "tag": "ModelDB:139657" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 573, - "tag": "Rebound firing" - } - ], - "timestamp_created": "2024-01-12 09:35:27.772003+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/139657", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1044": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1044, - "name": "Model of peristalsis in the dorsal blood vessel of Lumbriculus variegatus (Halfmann and Crisp 2011)", - "repository_type": "github", - "summary": "The blackworm, Lumbriculus variegatus, has a segmented dorsal blood vessel that acts as a peristaltic pump to move blood through its closed circulatory system. Here, we conducted a kinematic study using videography and computational modeling as a first step toward understanding the control of DBV pulsation. A simple feed-forward system of distributed, coupled neuronal oscillators is a sufficient model was a sufficient model to explain the control of pulsation in the blackworm.", - "tags": [ - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1545, - "tag": "ModelDB:139760" - }, - { - "id": 620, - "tag": "Python" - } - ], - "timestamp_created": "2024-01-12 09:35:28.315566+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/139760", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1045": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1045, - "name": "Reconstructing cerebellar granule layer evoked LFP using convolution (ReConv) (Diwakar et al. 2011)", - "repository_type": "github", - "summary": "The model allows reconstruction of evoked local field potentials as seen in the cerebellar granular layer. The approach uses a detailed model of cerebellar granule neuron to generate data traces and then uses a \"ReConv\" or jittered repetitive convolution technique to reproduce post-synaptic local field potentials in the granular layer. The algorithm was used to generate both in vitro and in vivo evoked LFP and reflected the changes seen during LTP and LTD, when such changes were induced in the underlying neurons by modulating release probability of synapses and sodium channel regulated intrinsic excitability of the cells.", - "tags": [ - { - "id": 1546, - "tag": "Evoked LFP" - }, - { - "id": 734, - "tag": "Extracellular Fields" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 1531, - "tag": "I Cl, leak" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1547, - "tag": "ModelDB:139883" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 779, - "tag": "Octave" - } - ], - "timestamp_created": "2024-01-12 09:35:28.857371+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/139883", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1046": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1046, - "name": "Hyperconnectivity, slow synapses in PFC mental retardation and autism model (Testa-Silva et al 2011)", - "repository_type": "github", - "summary": "The subdirectory 'matlab' contains MATLAB scripts (The Mathworks, USA) that can be used to reproduce the panels of Figures 4-5.\r\n\r\nThis directory contains files to reproduce sample computer simulations presented in the 2011 paper authored by\r\n\r\nMeredith, R., Testa-Silva, G., Loebel, A., Giugliano, M., de Kock, C.; Mansvelder, H. \r\n\"Hyperconnectivity and slow synapses in prefrontal cortex of a model for mental retardation and autism\".\r\n\r\n\r\nABSTRACT\r\n\"... We propose that these findings are tightly linked: using a network model, we show that slower synapses are essential to counterbalance hyperconnectivity in order to maintain a dynamic range of excitatory activity. However, the slow synaptic time constants induce decreased responsiveness to low frequency stimulation, which may explain deficits in integration and information processing in attentional neuronal networks in neurodevelopmental disorders.\"", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 718, - "tag": "Attractor Neural Network" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1548, - "tag": "ModelDB:140033" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - } - ], - "timestamp_created": "2024-01-12 09:35:29.432767+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/140033", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1047": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1047, - "name": "Dorsal root ganglion (DRG) neuronal model (Kovalsky et al. 2009)", - "repository_type": "github", - "summary": "This model, diverged from oscillatory parameters seen in live cells and failed to produce characteristic ectopic discharge patterns. Here we show that use of a more complete set of Na+ conductances--which includes several delayed components--enables simulation of the entire repertoire of oscillation-triggered electrogenic phenomena seen in live dorsal root ganglion (DRG) neurons. This includes a physiological window of induction and natural patterns of spike discharge. An INa+ component at 2-20 ms was particularly important, even though it represented only a tiny fraction of overall INa+ amplitude. With the addition of a delayed rectifier IK+ the singlet firing seen in some DRG neurons can also be simulated. The model reveals the key conductances that underlie afferent ectopia, conductances that are potentially attractive targets in the search for more effective treatments of neuropathic pain.", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 819, - "tag": "Late Na" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1549, - "tag": "ModelDB:140038" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 787, - "tag": "Pathophysiology" - } - ], - "timestamp_created": "2024-01-12 09:35:29.983448+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/140038", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1048": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 1048, - "name": "A simple integrative electrophysiological model of bursting GnRH neurons (Csercsik et al. 2011)", - "repository_type": "github", - "summary": "In this paper a modular model of the GnRH\r\nneuron is presented. For the aim of simplicity, the\r\ncurrents corresponding to fast time scales and action\r\npotential generation are described by an impulsive system,\r\nwhile the slower currents and calcium dynamics\r\nare described by usual ordinary differential equations\r\n(ODEs). The model is able to reproduce the depolarizing\r\nafterpotentials, afterhyperpolarization, periodic\r\nbursting behavior and the corresponding calcium transients\r\nobserved in the case of GnRH neurons.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1550, - "tag": "ModelDB:140246" - } - ], - "timestamp_created": "2024-01-12 09:35:30.526398+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/140246", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1049": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1049, - "name": "A multi-compartment model for interneurons in the dLGN (Halnes et al. 2011)", - "repository_type": "github", - "summary": "This model for dLGN interneurons is presented in two parameterizations (P1 & P2), which were fitted to current-clamp data from two different interneurons (IN1 & IN2). The model qualitatively reproduces the responses in IN1 & IN2 under 8 different experimental condition, and quantitatively reproduces the I/O-relations (#spikes elicited as a function of injected current).", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 731, - "tag": "I CAN" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 805, - "tag": "I Mixed" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 1433, - "tag": "I_AHP" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1551, - "tag": "ModelDB:140249" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 573, - "tag": "Rebound firing" - } - ], - "timestamp_created": "2024-01-12 09:35:31.045114+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/140249", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1050": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1050, - "name": "Firing patterns in stuttering fast-spiking interneurons (Klaus et al. 2011)", - "repository_type": "github", - "summary": "This is a morphologically extended version of the fast-spiking interneuron by Golomb et al. (2007).\r\n\r\nThe model captures the stuttering firing pattern and subthreshold oscillations in response to step current input as observed in many cortical and striatal fast-spiking cells.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1552, - "tag": "ModelDB:140254" - }, - { - "id": 1553, - "tag": "PGENESIS" - }, - { - "id": 834, - "tag": "Stuttering" - } - ], - "timestamp_created": "2024-01-12 09:35:31.626438+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/140254", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1051": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1051, - "name": "Four cortical interneuron subtypes (Kubota et al. 2011)", - "repository_type": "github", - "summary": "\" ... Using electron microscopy and serial reconstructions, we analyzed the dendritic trees of\r\nfour morphologically distinct neocortical interneuron subtypes to reveal two underlying organizational\r\nprinciples common to all.\r\n\r\nFirst, cross-sectional areas at any given point within a dendrite were proportional\r\nto the summed length of all dendritic segments distal to that point.\r\n\r\n...\r\nSecond, dendritic cross-sections\r\nbecame progressively more elliptical at more proximal, larger diameter, dendritic locations.\r\n\r\nFinally,\r\ncomputer simulations revealed that these conserved morphological features limit distance dependent\r\nfiltering of somatic EPSPs and facilitate distribution of somatic depolarization into all dendritic\r\ncompartments.\r\n...\"", - "tags": [ - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1554, - "tag": "ModelDB:140299" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:35:32.140370+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/140299", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1052": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1052, - "name": "Calcium and potassium currents of olfactory bulb juxtaglomerular cells (Masurkar and Chen 2011)", - "repository_type": "github", - "summary": "Inward and outward currents of the olfactory bulb juxtaglomerular cells are characterized in the experiments and modeling in these two Masurkar and Chen 2011 papers.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 769, - "tag": "I_KHT" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1555, - "tag": "ModelDB:140462" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - } - ], - "timestamp_created": "2024-01-12 09:35:32.663724+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/140462", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1053": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1053, - "name": "Cancelling redundant input in ELL pyramidal cells (Bol et al. 2011)", - "repository_type": "github", - "summary": "The paper investigates the property of the electrosensory lateral line lobe (ELL) of the brain of weakly electric fish to cancel predictable stimuli. Electroreceptors on the skin encode all signals in their firing activity, but superficial pyramidal (SP) cells in the ELL that receive this feedforward input do not respond to constant sinusoidal signals. This cancellation putatively occurs using a network of feedback delay lines and burst-induced synaptic plasticity between the delay lines and the SP cell that learns to cancel the redundant input. Biologically, the delay lines are parallel fibres from cerebellar-like granule cells in the eminentia granularis posterior.\r\n\r\nA model of this network (e.g. electroreceptors, SP cells, delay lines and burst-induced plasticity) was constructed to test whether the current knowledge of how the network operates is sufficient to cancel redundant stimuli. \r\n", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 857, - "tag": "Biofeedback" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 825, - "tag": "Learning" - }, - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1556, - "tag": "ModelDB:140465" - }, - { - "id": 874, - "tag": "Noise Sensitivity" - }, - { - "id": 802, - "tag": "STDP" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 803, - "tag": "Unsupervised Learning" - } - ], - "timestamp_created": "2024-01-12 09:35:33.462999+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/140465", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1054": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1054, - "name": "Shaping of action potentials by different types of BK channels (Jaffe et al., 2011)", - "repository_type": "github", - "summary": "Dentate gyrus granule cells highly express the beta4 accessory subunit which confer BK channels with type II properties. The properties of heterologously-expressed BK channels (with and without the beta4 subunit) were used to construct channel models. These were then used to study how they affect single action potentials and trains of spikes in a model dentate gyrus granule cells (based on Aradi and Holmes, 1999).", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1557, - "tag": "ModelDB:140471" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:35:33.992373+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/140471", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1055": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1055, - "name": "Dipole Localization Kit (Mechler & Victor, 2012)", - "repository_type": "github", - "summary": "We localize a single neuron from the spatial sample of its EAP\r\namplitudes recorded with a multisite probe (with 6 or more independent\r\nmeasurement sites or channels, e.g., a silicon polytrode, a stepped\r\ntetrode, etc.) This is an inverse problem and we solve it by fitting a\r\nmodel to the EAPs that consists of a volume conductor model of the\r\nneural tissue (known), a realistic model of the probe (known), and a\r\nsingle dipole current source of the model neuron (unknown). The dipole\r\nis free to change position, size, and orientation (a total of 6\r\nparameters) at each moment during the action potential.", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1558, - "tag": "ModelDB:140599" - } - ], - "timestamp_created": "2024-01-12 09:35:34.512896+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/140599", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1056": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1056, - "name": "CA3 Radiatum/Lacunosum-Moleculare interneuron, Ih (Anderson et al. 2011)", - "repository_type": "github", - "summary": "\"The present study examines the biophysical\r\nproperties and functional implications of Ih in hippocampal\r\narea CA3 interneurons with somata in strata radiatum and\r\nlacunosum-moleculare.... The functional\r\nconsequences of Ih were examined with regard to temporal\r\nsummation and impedance measurements. ...\r\nFrom impedance measurements, we\r\nfound that Ih did not confer theta-band resonance, but\r\nflattened the impedance\u2013frequency relations instead. ... Finally, a model of Ih was employed in\r\ncomputational analyses to confirm and elaborate upon the\r\ncontributions of Ih to impedance and temporal summation.\"", - "tags": [ - { - "id": 594, - "tag": "I h" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1559, - "tag": "ModelDB:140732" - } - ], - "timestamp_created": "2024-01-12 09:35:35.015014+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/140732", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1057": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1057, - "name": "Motoneuron model of self-sustained firing after spinal cord injury (Kurian et al. 2011)", - "repository_type": "github", - "summary": "\" ...\r\nDuring the acute-stage of spinal cord injury (SCI), the endogenous ability to generate plateaus is lost; however, during the chronic-stage of SCI, plateau potentials reappear with prolonged self-sustained firing that has been implicated in the development of spasticity. In this work, we extend previous modeling studies to systematically investigate the mechanisms underlying the generation of plateau potentials in motoneurons, including the influences of specific ionic currents, the morphological characteristics of the soma and dendrite, and the interactions between persistent inward currents and synaptic input.\r\n...\"", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 731, - "tag": "I CAN" - }, - { - "id": 1560, - "tag": "I Ca,p" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 763, - "tag": "Intrinsic plasticity" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1561, - "tag": "ModelDB:140788" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 759, - "tag": "XPPAUT" - } - ], - "timestamp_created": "2024-01-12 09:35:35.532501+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/140788", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1058": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1058, - "name": "Detailed passive cable model of Dentate Gyrus Basket Cells (Norenberg et al. 2010)", - "repository_type": "github", - "summary": "Fast-spiking, parvalbumin-expressing basket cells (BCs) play a key role in feedforward and feedback inhibition in the hippocampus. \r\n...\r\nTo quantitatively address this question, we developed detailed passive cable models of BCs in the dentate gyrus based on dual somatic or somatodendritic recordings and complete morphologic reconstructions. \r\n\r\nBoth specific membrane capacitance and axial resistivity were comparable to those of pyramidal neurons, but the average somatodendritic specific membrane resistance (R(m)) was substantially lower in BCs. \r\n\r\nFurthermore, R(m) was markedly nonuniform, being lowest in soma and proximal dendrites, intermediate in distal dendrites, and highest in the axon. \r\n...\r\nFurther computational analysis revealed that these unique cable properties accelerate the time course of synaptic potentials at the soma in response to fast inputs, while boosting the efficacy of slow distal inputs. \r\n\r\nThese properties will facilitate both rapid phasic and efficient tonic activation of BCs in hippocampal microcircuits.\r\n", - "tags": [ - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1562, - "tag": "ModelDB:140789" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 568, - "tag": "Parameter Fitting" - } - ], - "timestamp_created": "2024-01-12 09:35:36.062333+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/140789", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1059": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1059, - "name": "Dendritic Discrimination of Temporal Input Sequences (Branco et al. 2010)", - "repository_type": "github", - "summary": "Compartmental model of a layer 2/3 pyramidal cell in the rat somatosensory cortex, exploring NMDA-dependent sensitivity to the temporal sequence of synaptic activation.", - "tags": [ - { - "id": 860, - "tag": "Direction Selectivity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1563, - "tag": "ModelDB:140828" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 596, - "tag": "Synaptic Integration" - } - ], - "timestamp_created": "2024-01-12 09:35:36.565538+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/140828", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1060": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1060, - "name": "Computational Surgery (Lytton et al. 2011)", - "repository_type": "github", - "summary": "Figure 2 in\r\n Neocortical simulation for epilepsy surgery guidance: Localization and intervention,\r\n by William W. Lytton, Samuel A. Neymotin, Jason C. Wester, and Diego Contreras\r\n in Computational Surgery and Dual Training, Springer, 2011\r\n", - "tags": [ - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1564, - "tag": "ModelDB:140881" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:35:37.068082+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/140881", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1061": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 1061, - "name": "Event-related simulation of neural processing in complex visual scenes (Mihalas et al. 2011)", - "repository_type": "github", - "summary": "\"... We here present an environment for the\r\nimplementation of large networks of generalized integrate-and-fire\r\nneurons which uses an asynchronous event-based algorithm.\r\n...\r\nThe neuronal network to be simulated\r\nand all parameters are defined in extendible markup language.\r\nA model of the primate early visual system is implemented. The\r\nuse of the tool is illustrated by simulating the processing of both\r\nsimple and complex visual scenes through retina, thalamus and\r\nprimary visual cortex.\"", - "tags": [ - { - "id": 1565, - "tag": "ERNST (Event Related Neuronal Simulation Tool) (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1566, - "tag": "ModelDB:140964" - }, - { - "id": 758, - "tag": "Sensory processing" - }, - { - "id": 726, - "tag": "Vision" - }, - { - "id": 829, - "tag": "XML (web link to model)" - } - ], - "timestamp_created": "2024-01-12 09:35:37.628352+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/140964", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1062": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1062, - "name": "Measuring neuronal identification quality in ensemble recordings (isoitools) (Neymotin et al. 2011)", - "repository_type": "github", - "summary": "\"... Here we describe information theoretic\r\nmeasures of action potential waveform isolation applicable to any dataset, that have an intuitive,\r\nuniversal interpretation, and that are not dependent on the methods or choice of parameters for\r\nsingle unit isolation, and that have been validated using a dataset.\"", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 734, - "tag": "Extracellular Fields" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1567, - "tag": "ModelDB:141061" - } - ], - "timestamp_created": "2024-01-12 09:35:38.216873+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/141061", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1063": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1063, - "name": "Relative spike time coding and STDP-based orientation selectivity in V1 (Masquelier 2012)", - "repository_type": "github", - "summary": "Phenomenological spiking model of the cat early visual system. We show how natural vision can drive spike time correlations on sufficiently fast time scales to lead to the acquisition of orientation-selective V1 neurons through STDP. This is possible without reference times such as stimulus onsets, or saccade landing times. But even when such reference times are available, we demonstrate that the relative spike times encode the images more robustly than the absolute ones.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 595, - "tag": "Coincidence Detection" - }, - { - "id": 793, - "tag": "Development" - }, - { - "id": 1478, - "tag": "Information transfer" - }, - { - "id": 825, - "tag": "Learning" - }, - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1568, - "tag": "ModelDB:141062" - }, - { - "id": 1569, - "tag": "Orientation selectivity" - }, - { - "id": 719, - "tag": "Pattern Recognition" - }, - { - "id": 802, - "tag": "STDP" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 803, - "tag": "Unsupervised Learning" - }, - { - "id": 726, - "tag": "Vision" - }, - { - "id": 822, - "tag": "Winner-take-all" - } - ], - "timestamp_created": "2024-01-12 09:35:39.107943+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/141062", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1064": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1064, - "name": "Role of Ih in firing patterns of cold thermoreceptors (Orio et al., 2012)", - "repository_type": "github", - "summary": "\" ... Here we investigated the role of Ih in cold-sensitive (CS) nerve endings, where cold sensory transduction actually takes place. Corneal CS nerve endings in mice show a rhythmic spiking activity at neutral skin temperature that switches to bursting mode when the temperature is lowered. \r\n...\r\nMathematical modeling shows that the firing phenotype of CS nerve endings from HCN1-/- mice can be reproduced by replacing HCN1 channels with the slower HCN2 channels rather than by abolishing Ih. We propose that Ih carried by HCN1 channels helps tune the frequency of the oscillation and the length of bursts underlying regular spiking in cold thermoreceptors, having important implications for neural coding of cold sensation. \r\n\"", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1570, - "tag": "ModelDB:141063" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 821, - "tag": "Sensory coding" - }, - { - "id": 758, - "tag": "Sensory processing" - }, - { - "id": 569, - "tag": "Simplified Models" - } - ], - "timestamp_created": "2024-01-12 09:35:39.885742+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/141063", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1065": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1065, - "name": "A four compartmental model for ABPD complex in crustacean pyloric network (Maran et al. 2011)", - "repository_type": "github", - "summary": "\"Central pattern generators (CPGs) frequently include bursting neurons that serve as pacemakers for rhythm generation.\r\n\r\nPhase resetting curves (PRCs) can provide insight into mechanisms underlying phase locking in such circuits.\r\n\r\nPRCs were constructed for a pacemaker bursting complex in the pyloric circuit in the stomatogastric ganglion of the lobster and crab.\r\n...\"", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1571, - "tag": "ModelDB:141192" - } - ], - "timestamp_created": "2024-01-12 09:35:40.469364+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/141192", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1066": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1066, - "name": "Multiscale interactions between chemical and electric signaling in LTP (Bhalla 2011)", - "repository_type": "github", - "summary": "\"Synaptic plasticity leads to long-term changes in excitability, whereas cellular homeostasis maintains\r\nexcitability. Both these processes involve interactions between molecular events, electrical events, and\r\nnetwork activity. Here I explore these intersections with a multilevel model that embeds molecular events\r\nfollowing synaptic calcium influx into a multicompartmental electrical model of a CA1 hippocampal\r\nneuron. ...\"", - "tags": [ - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 75, - "tag": "Homeostasis" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 1433, - "tag": "I_AHP" - }, - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1572, - "tag": "ModelDB:141226" - }, - { - "id": 866, - "tag": "Multiscale" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - } - ], - "timestamp_created": "2024-01-12 09:35:41.023196+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/141226", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1067": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1067, - "name": "Cerebellar long-term depression (LTD) (Antunes and De Schutter 2012)", - "repository_type": "github", - "summary": "Many cellular processes involve small number of molecules and undergo stochastic fluctuations in their levels of activity. Among these processes is cerebellar long-term depression (LTD), a form of synaptic plasticity expressed as a reduction in the number of synaptic AMPA receptors (AMPARs) in Purkinje cells. Using a stochastic model of the signaling network and mechanisms of AMPAR trafficking involved in LTD, we show that the network activity in single synapses switches between two discrete stable states (LTD and non-LTD). Stochastic fluctuations affecting more intensely the level of activity of a few components of the network lead to the probabilistic induction of LTD and threshold dithering. The non-uniformly distributed stochasticity of the network allows the stable occurrence of several different macroscopic levels of depression, determining the experimentally observed sigmoidal relationship between the magnitude of depression and the concentration of the triggering signal.", - "tags": [ - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1573, - "tag": "ModelDB:141270" - }, - { - "id": 1574, - "tag": "STEPS" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - } - ], - "timestamp_created": "2024-01-12 09:35:41.567943+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/141270", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1068": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1068, - "name": "Simple and accurate Diffusion Approximation algor. for stochastic ion channels (Orio & Soudry 2012)", - "repository_type": "github", - "summary": "\" ... We derived the (Stochastic Differential Equations) SDE explicitly for any given ion channel kinetic scheme. The resulting generic equations were surprisingly simple and interpretable \u2013 allowing an easy, transparent and efficient (Diffusion Approximation) DA implementation, avoiding unnecessary approximations. The algorithm was tested in a voltage clamp simulation and in two different current clamp simulations, yielding the same results as (Markov Chains) MC modeling. Also, the simulation efficiency of this DA method demonstrated considerable superiority over MC methods, except when short time steps or low channel numbers were used.\"", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 748, - "tag": "Markov-type model" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1575, - "tag": "ModelDB:141272" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 816, - "tag": "SciLab" - }, - { - "id": 1576, - "tag": "Stochastic simulation" - } - ], - "timestamp_created": "2024-01-12 09:35:42.348132+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/141272", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1069": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1069, - "name": "Engaging distinct oscillatory neocortical circuits (Vierling-Claassen et al. 2010)", - "repository_type": "github", - "summary": "\"Selective optogenetic drive of fast-spiking (FS) interneurons (INs) leads to enhanced local field potential (LFP) power across the traditional \u201cgamma\u201d frequency band (20\u201380 Hz; Cardin et al., 2009).\r\n\r\nIn contrast, drive to regular-spiking (RS) pyramidal cells enhances power at lower frequencies, with a peak at 8 Hz.\r\n\r\nThe first result is consistent with previous computational studies emphasizing the role of FS and the time constant of GABAA synaptic inhibition in gamma rhythmicity.\r\n\r\nHowever, the same theoretical models do not typically predict low-frequency LFP enhancement with RS drive.\r\n\r\nTo develop hypotheses as to how the same network can support these contrasting behaviors, we constructed a biophysically principled network model of primary somatosensory neocortex containing FS, RS, and low-threshold spiking (LTS) INs. ...\"", - "tags": [ - { - "id": 1527, - "tag": "Brain Rhythms" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 1546, - "tag": "Evoked LFP" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1577, - "tag": "ModelDB:141273" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 764, - "tag": "Touch" - } - ], - "timestamp_created": "2024-01-12 09:35:42.985803+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/141273", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1070": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1070, - "name": "Biophysically detailed model of the mouse sino-atrial node cell (Kharche et al. 2011)", - "repository_type": "github", - "summary": "This model is developed to study the role of various electrophysiological mechanisms in generating cardiac pacemaking action potentials (APs).The model incorporates membrane ionic currents and intracellular mechanisms contributing to spontaneous mouse SAN APs. The model was validated by testing the functional roles of individual membrane currents in one and multiple parameter analyses.The roles of intracellular Ca2+-handling mechanisms on cardiac pacemaking were also investigated in the model.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 797, - "tag": "Cardiac pacemaking" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 789, - "tag": "I_HERG" - }, - { - "id": 784, - "tag": "KCNQ1" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1578, - "tag": "ModelDB:141274" - }, - { - "id": 738, - "tag": "Na/Ca exchanger" - }, - { - "id": 740, - "tag": "Na/K pump" - }, - { - "id": 585, - "tag": "Oscillations" - } - ], - "timestamp_created": "2024-01-12 09:35:43.533805+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/141274", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1071": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1071, - "name": "Prosthetic electrostimulation for information flow repair in a neocortical simulation (Kerr 2012)", - "repository_type": "github", - "summary": "This model is an extension of a model ( http://modeldb.yale.edu/138379 ) recently published in Frontiers in Computational Neuroscience. This model consists of 4700 event-driven, rule-based neurons, wired according to anatomical data, and driven by both white-noise synaptic inputs and a sensory signal recorded from a rat thalamus. Its purpose is to explore the effects of cortical damage, along with the repair of this damage via a neuroprosthesis.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 1527, - "tag": "Brain Rhythms" - }, - { - "id": 831, - "tag": "Deep brain stimulation" - }, - { - "id": 765, - "tag": "I Chloride" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 1478, - "tag": "Information transfer" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1579, - "tag": "ModelDB:141505" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:35:44.047013+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/141505", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1072": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1072, - "name": "Network model with neocortical architecture (Anderson et al 2007,2012; Azhar et al 2012)", - "repository_type": "github", - "summary": "Architecturally realistic neocortical model using seven classes of excitatory and inhibitory single compartment Hodgkin-Huxley cells. This is an addendum to ModelDB Accession # 98902, Studies of stimulus parameters for seizure disruption (Anderson et al. 2007). Wiring is adapted from the minicolumn hypothesis and incorporates visual and neocortical wiring data. Simulation demonstrates spontaneous bursting onset and cessation. This activity can be induced by random fluctuations in the surrounding background input.", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1580, - "tag": "ModelDB:141507" - }, - { - "id": 726, - "tag": "Vision" - } - ], - "timestamp_created": "2024-01-12 09:35:44.559396+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/141507", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1073": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1073, - "name": "Basal ganglia-thalamic network model for deep brain stimulation (So et al. 2012)", - "repository_type": "github", - "summary": "This is a model of the basal ganglia-thalamic network, modified from the Rubin and Terman model (High frequency stimulation of the Subthalamic Nucleus, Rubin and Terman 2004). We subsequently used this model to investigate the effectiveness of STN and GPi DBS as well as lesion when various proportions of local cells and fibers of passage were activated or silenced. The BG network exhibited characteristics consistent with published experimental data, both on the level of single cells and on the network level. Perhaps most notably, and in contrast to the original RT model, the changes in the thalamic error index with changes in the DBS frequency matched well the changes in clinical symptoms with changes in DBS frequency.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 831, - "tag": "Deep brain stimulation" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 1531, - "tag": "I Cl, leak" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1581, - "tag": "ModelDB:141699" - }, - { - "id": 801, - "tag": "Parkinson's" - } - ], - "timestamp_created": "2024-01-12 09:35:45.174600+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/141699", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1074": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 1074, - "name": "Mathematical Foundations of Neuroscience (Ermentrout and Terman 2010)", - "repository_type": "github", - "summary": "Ermentrout and Terman's book on dynamical systems and computational methods in neuroscience has associated computer code available at http://www.math.pitt.edu/~bard/bardware/neurobook/allodes.html. The main emphasis in the book is on single-neuron biophysics and there is also systems neuroscience theory and applications to networks. The electronic text of the book is freely available!", - "tags": [ - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1582, - "tag": "ModelDB:141835" - }, - { - "id": 732, - "tag": "Tutorial/Teaching" - }, - { - "id": 830, - "tag": "XPP (web link to model)" - } - ], - "timestamp_created": "2024-01-12 09:35:45.743415+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/141835", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1075": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1075, - "name": "Two Models for synaptic input statistics for the MSO neuron model (Jercog et al. 2010)", - "repository_type": "github", - "summary": "The model is a point neuron model with ionic currents from Rothman & Mannis (2003) and with an update of the low threshold potassium current (IKLT) measured in-vitro by Mathews & Jercog et al (2010).\r\nThis model in conjunction with the synaptic input models presented here has been used to gain insight into mechanisms that account for experimentally observed asymmetries in ITD tuning (Brand et al, 2002).\r\nAsymmetry and displacement of the ITD response function is achieved in the model by the interplay between asymmetry of the excitatory inputs arriving from the two sides and the precise voltage dependent activation of IKLT.\r\nIn Jercog et al (2010) we propose two different mathematical ways, physiologically plausible scenarios, of generating the asymmetry in the bilateral synaptic input events. Here, we present two models for simulating the stochastic synaptic input trains.", - "tags": [ - { - "id": 762, - "tag": "Audition" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1583, - "tag": "ModelDB:141991" - }, - { - "id": 1584, - "tag": "Synaptic-input statistic" - } - ], - "timestamp_created": "2024-01-12 09:35:46.419240+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/141991", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1076": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 1076, - "name": "Efficient simulation environment for modeling large-scale cortical processing (Richert et al. 2011)", - "repository_type": "github", - "summary": "\"We have developed a spiking neural network simulator, which is both easy to use and computationally efficient, for the generation of large-scale computational neuroscience models. The simulator implements current or conductance based Izhikevich neuron networks, having spike-timing dependent plasticity and short-term plasticity. ...\"", - "tags": [ - { - "id": 776, - "tag": "C or Cplusplus program (web link to model)" - }, - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1585, - "tag": "ModelDB:142062" - }, - { - "id": 802, - "tag": "STDP" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - } - ], - "timestamp_created": "2024-01-12 09:35:47.017385+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/142062", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1077": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1077, - "name": "Hippocampal CA3 network and circadian regulation (Stanley et al. 2013)", - "repository_type": "github", - "summary": "This model produces the hippocampal CA3 neural network model used \r\nin the paper below. It has two modes of operation, a default mode and a circadian mode. In the circadian mode, parameters are swept through a range of values. \r\nThis model can be quite easily adapted to produce theta and gamma oscillations, as certain parameter sweeps will reveal (see Figures). BASH scripts interact with GENESIS\r\n2.3 to implement parameter sweeps.\r\nThe model contains four cell types derived from prior papers.\r\nCA3 pyramidal are derived from Traub et al (1991); Basket, \r\nstratum oriens (O-LM), and Medial Septal GABAergic\r\n(MSG) interneurons are taken from Hajos et al (2004).", - "tags": [ - { - "id": 1527, - "tag": "Brain Rhythms" - }, - { - "id": 1586, - "tag": "Circadian Rhythms" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1587, - "tag": "ModelDB:142104" - } - ], - "timestamp_created": "2024-01-12 09:35:47.536905+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/142104", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1078": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1078, - "name": "Rate model of a cortical RS-FS-LTS network (Hayut et al. 2011)", - "repository_type": "github", - "summary": "A rate model of cortical networks composed of RS, FS and LTS neurons. Synaptic depression is modelled according to the Tsodyks-Markram scheme.", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1588, - "tag": "ModelDB:142199" - }, - { - "id": 791, - "tag": "Rate-coding model neurons" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - } - ], - "timestamp_created": "2024-01-12 09:35:48.068978+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/142199", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1079": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1079, - "name": "FRAT: An amygdala-centered model of fear conditioning (Krasne et al. 2011)", - "repository_type": "github", - "summary": "Model of Pavlovian fear conditioning and extinction (due to neuromodulator-controlled LTP on principal cells and inhibory interneurons)occur in amygdala and contextual representations are learned in hippocampus. Many properties of fear conditioning are accounted for.", - "tags": [ - { - "id": 825, - "tag": "Learning" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1589, - "tag": "ModelDB:142273" - } - ], - "timestamp_created": "2024-01-12 09:35:48.561945+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/142273", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1080": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1080, - "name": "Ion concentration dynamics as a mechanism for neuronal bursting (Barreto & Cressman 2011)", - "repository_type": "github", - "summary": "\"We describe a simple conductance-based model neuron that includes intra and\r\nextracellular ion concentration dynamics and show that this model exhibits periodic\r\nbursting. The bursting arises as the fast-spiking behavior of the neuron is modulated\r\nby the slow oscillatory behavior in the ion concentration variables and vice versa. By\r\nseparating these time scales and studying the bifurcation structure of the neuron, we catalog\r\nseveral qualitatively different bursting profiles that are strikingly similar to those seen in\r\nexperimental preparations. Our work suggests that ion concentration dynamics may play an\r\nimportant role in modulating neuronal excitability in real biological systems.\"", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 873, - "tag": "Depolarization block" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1590, - "tag": "ModelDB:142630" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 759, - "tag": "XPPAUT" - } - ], - "timestamp_created": "2024-01-12 09:35:49.126971+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/142630", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1081": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1081, - "name": "Fisher and Shannon information in finite neural populations (Yarrow et al. 2012)", - "repository_type": "github", - "summary": "Here we model populations of rate-coding neurons with bell-shaped tuning curves and multiplicative Gaussian noise. This Matlab code supports the calculation of information theoretic (mutual information, stimulus-specific information, stimulus-specific surprise) and Fisher-based measures (Fisher information, I_Fisher, SSI_Fisher) in these population models. The information theoretic measures are computed by Monte Carlo integration, which allows computationally-intensive decompositions of the mutual information to be computed for relatively large populations (hundreds of neurons).", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1591, - "tag": "ModelDB:142990" - }, - { - "id": 791, - "tag": "Rate-coding model neurons" - } - ], - "timestamp_created": "2024-01-12 09:35:49.764652+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/142990", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1082": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1082, - "name": "ClC-2 channels regulate neuronal excitability, not intracellular Cl- levels (Ratte & Prescott 2011)", - "repository_type": "github", - "summary": "\"The model is for a generic, single compartment neuron with multiple ion currents. The most notable mechanisms include ClC-2 (a rectifying chloride-leak channel) and KCC2 (potassium chloride co-transporter 2). A significant feature of the model is that it tracks intracellular chloride concentration. Moreover, the GABA-A receptor is modeled as passing both chloride and bicarbonate ions, which is important for proper calculation of the GABA reversal potential. Ornstein-Unlenbeck processes to simulate synaptic inhibition and excitation are also included.\"\r\n", - "tags": [ - { - "id": 1592, - "tag": "Chloride regulation" - }, - { - "id": 1531, - "tag": "I Cl, leak" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1593, - "tag": "ModelDB:142993" - }, - { - "id": 759, - "tag": "XPPAUT" - } - ], - "timestamp_created": "2024-01-12 09:35:50.259566+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/142993", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1083": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1083, - "name": "Explaining pathological changes in axonal excitability by dynamical analysis (Coggan et al. 2011)", - "repository_type": "github", - "summary": "\"... To help decipher the biophysical basis for \u2018paroxysmal\u2019\r\nspiking, we replicated afterdischarge (i.e. continued spiking after a brief stimulus) in a\r\nminimal conductance-based axon model.\r\n\r\n...\r\nA perturbation\r\ncould abruptly switch the system between two (quasi-)stable attractor states: rest and\r\nrepetitive spiking.\r\n...\r\n Initiation of afterdischarge was explained by activation of the\r\npersistent inward current forcing the system to cross a saddle point that separates the basins of\r\nattraction associated with each attractor.\r\n\r\nTermination of afterdischarge was explained by the\r\nattractor associated with repetitive spiking being destroyed.\r\n...\r\nThe model also explains other features of\r\nparoxysmal symptoms, including temporal summation and refractoriness.\"", - "tags": [ - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1594, - "tag": "ModelDB:143072" - }, - { - "id": 775, - "tag": "Nociception" - }, - { - "id": 759, - "tag": "XPPAUT" - } - ], - "timestamp_created": "2024-01-12 09:35:50.797602+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/143072", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1084": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1084, - "name": "STDP and oscillations produce phase-locking (Muller et al. 2011)", - "repository_type": "github", - "summary": "\"... In this note, we investigate a simple mechanism for learning precise LFP-to-spike coupling in feed-forward networks \u2013 the reliable, periodic modulation of presynaptic firing rates during oscillations, coupled with spike-timing dependent plasticity. When oscillations are within the biological range (2\u2013150 Hz), firing rates of the inputs change on a timescale highly relevant to spike-timing dependent plasticity (STDP). Through analytic and computational methods, we find points of stable phase-locking for a neuron with plastic input synapses.\r\n\r\nThese points correspond to precise phase-locking behavior in the feed-forward network. The location of these points depends on the oscillation frequency of the inputs, the STDP time constants, and the balance of potentiation and de-potentiation in the STDP rule.\r\n...\"\r\n", - "tags": [ - { - "id": 645, - "tag": "Brian" - }, - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1595, - "tag": "ModelDB:143083" - }, - { - "id": 620, - "tag": "Python" - } - ], - "timestamp_created": "2024-01-12 09:35:51.292953+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/143083", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1085": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1085, - "name": "Phase response curve of a globus pallidal neuron (Fujita et al. 2011)", - "repository_type": "github", - "summary": "We investigated how changes in ionic conductances alter the phase response curve (PRC) of a globus pallidal (GP) neuron and stability of a synchronous activity of a GP network, using a single-compartmental conductance-based neuron model. The results showed the PRC and the stability were influenced by changes in the persistent sodium current, the Kv3 potassium, the M-type potassium and the calcium-dependent potassium current.", - "tags": [ - { - "id": 590, - "tag": "I A" - }, - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 784, - "tag": "KCNQ1" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1596, - "tag": "ModelDB:143100" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 823, - "tag": "Phase Response Curves" - }, - { - "id": 586, - "tag": "Synchronization" - } - ], - "timestamp_created": "2024-01-12 09:35:51.853484+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/143100", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1086": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1086, - "name": "Effects of the membrane AHP on the Lateral Superior Olive (LSO) (Zhou & Colburn 2010)", - "repository_type": "github", - "summary": "This simulation study investigated how membrane afterhyperpolarization (AHP) influences spiking activity of neurons in the Lateral Superior Olive (LSO). The model incorporates a general integrate-and-fire spiking mechanism with a first-order adaptation channel. Simulations focus on differentiating the effects of GAHP, tauAHP, and input strength on (1) spike interval statistics, such as negative serial correlation and chopper onset, and (2) neural sensitivity to interaural level difference (ILD) of LSO neurons. The model simulated electrophysiological data collected in cat LSO (Tsuchitani and Johnson, 1985).", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 762, - "tag": "Audition" - }, - { - "id": 873, - "tag": "Depolarization block" - }, - { - "id": 1433, - "tag": "I_AHP" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1597, - "tag": "ModelDB:143114" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 777, - "tag": "Spike Frequency Adaptation" - } - ], - "timestamp_created": "2024-01-12 09:35:52.424272+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/143114", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1087": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1087, - "name": "Extraction and classification of three cortical neuron types (Mensi et al. 2012)", - "repository_type": "github", - "summary": "This script proposes a new convex fitting procedure that allows the parameters estimation of a large class of stochastic Integrate-and-Fire model upgraded with spike-triggered current and moving threshold from patch-clamp experiments (i.e. given the injected current and the recorded membrane potential).\r\nThis script applies the method described in the paper to estimate the parameters of a reference model from a single voltage trace and the corresponding input current and evaluate the performance of the fitted model on a separated test set.", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1598, - "tag": "ModelDB:143148" - } - ], - "timestamp_created": "2024-01-12 09:35:52.949356+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/143148", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1088": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1088, - "name": "Phase precession through acceleration of local theta rhythm (Castro & Aguiar 2011)", - "repository_type": "github", - "summary": "\"... Here we\r\npresent a biophysical spiking model for phase precession in\r\nhippocampal CA1 which focuses on the interaction between\r\nplace cells and local inhibitory interneurons.\r\n\r\nThe model\u2019s\r\nfunctional block is composed of a place cell (PC) connected\r\nwith a local inhibitory cell (IC) which is modulated by the\r\npopulation theta rhythm.\r\n\r\nBoth cells receive excitatory inputs\r\nfrom the entorhinal cortex (EC).\r\n...\"\r\n", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1599, - "tag": "ModelDB:143248" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 799, - "tag": "Place cell/field" - }, - { - "id": 586, - "tag": "Synchronization" - } - ], - "timestamp_created": "2024-01-12 09:35:53.463843+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/143248", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1089": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1089, - "name": "Effects of eugenol on the firing of action potentials in NG108-15 neurons (Huang et al. 2011)", - "repository_type": "github", - "summary": "\"Rationale: Eugenol (EUG, 4-allyl-2-methoxyphenol), the main component of essential oil extracted from cloves, has various uses in medicine because of its potential to modulate neuronal excitability. \r\n\r\nHowever, its effects on the ionic mechanisms remains incompletely understood. \r\n\r\nObjectives: We aimed to investigate EUG`s effects on neuronal ionic currents and excitability, especially on voltage-gated ion currents, and to verify the effects on a hyperexcitability-temporal lobe seizure model. \r\n\r\nMethods: With the aid of patch-clamp technology, we first investigated the effects of EUG on ionic currents in NG108-15 neuronal cells differentiated with cyclic AMP. We then used modified Pinsky-Rinzel simulation modeling to evaluate its effects on spontaneous action potentials (APs). \r\n\r\nFinally, we investigated its effects on pilocarpine-induced seizures in rats. \r\n\r\nResults: EUG depressed the transient and late components of INa in the neurons. \r\n\r\nIt not only increased the degree of INa inactivation, but specifically suppressed the non-inactivating INa (INa(NI)). \r\n\r\n... In addition, EUG diminished L-type Ca2+ current and delayed rectifier K+ current only at higher concentrations. EUG`s effects on APs frequency reduction was verified by the simulation modeling. \r\n\r\nIn pilocarpine-induced seizures, the EUG-treated rats showed no shorter seizure latency but a lower seizure severity and mortality than the control rats. \r\n\r\n...\r\nConclusion: The synergistic blocking effects of INa and INa(NI) contributes to the main mechanism through which EUG affects the firing of neuronal APs and modulate neuronal hyperexcitability such as pilocarpine-induced temporal lobe seizures.\"", - "tags": [ - { - "id": 795, - "tag": "ATP-senstive potassium current" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1600, - "tag": "ModelDB:143253" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 759, - "tag": "XPPAUT" - } - ], - "timestamp_created": "2024-01-12 09:35:53.964933+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/143253", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1090": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1090, - "name": "Spike repolarization in axon collaterals (Foust et al. 2011)", - "repository_type": "github", - "summary": "Voltage sensing dye experiments and simulations characterize the location and re-polarizing function of Kv1 channels in cortical neurons.\r\n\"... (the papers) results indicate that action potential-induced synaptic transmission may operate through a mix of analog\u2013digital transmission owing to the properties of Kv1 channels in axon collaterals and presynaptic boutons.\"", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 747, - "tag": "I_KD" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1601, - "tag": "ModelDB:143442" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:35:54.493915+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/143442", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1091": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 1091, - "name": "Simulations of oscillations in piriform cortex (Wilson & Bower 1992)", - "repository_type": "github", - "summary": "\"1. A large-scale computer model of the piriform cortex was\r\nconstructed on the basis of the known anatomic and physiological\r\norganization of this region.\r\n2. The oscillatory field potential and electroencephalographic\r\n(EEG) activity generated by the model was compared with actual\r\nphysiological results. The model was able to produce patterns of\r\nactivity similar to those recorded physiologically in response to\r\nboth weak and strong electrical shocks to the afferent input. The\r\nmodel also generated activity patterns similar to EEGs recorded in\r\nbehaving animals.\r\n3. ...\"", - "tags": [ - { - "id": 1546, - "tag": "Evoked LFP" - }, - { - "id": 734, - "tag": "Extracellular Fields" - }, - { - "id": 750, - "tag": "GENESIS (web link to model)" - }, - { - "id": 765, - "tag": "I Chloride" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1602, - "tag": "ModelDB:143446" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 585, - "tag": "Oscillations" - } - ], - "timestamp_created": "2024-01-12 09:35:55.007237+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/143446", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1092": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 1092, - "name": "Principles of Computational Modelling in Neuroscience (Book) (Sterratt et al. 2011)", - "repository_type": "github", - "summary": "\"... This book provides a step-by-step account of how to model the neuron and neural circuitry to understand the nervous system at all levels, from ion channels to networks. Starting with a simple model of the neuron as an electrical circuit, gradually more details are added to include the effects of neuronal morphology, synapses, ion channels and intracellular signaling. The principle of abstraction is explained through chapters on simplifying models, and how simplified models can be used in networks. This theme is continued in a final chapter on modeling the development of the nervous system. Requiring an elementary background in neuroscience and some high school mathematics, this textbook is an ideal basis for a course on computational neuroscience.\"", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 831, - "tag": "Deep brain stimulation" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1603, - "tag": "ModelDB:143602" - }, - { - "id": 743, - "tag": "NEURON (web link to model)" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 732, - "tag": "Tutorial/Teaching" - } - ], - "timestamp_created": "2024-01-12 09:35:55.577567+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/143602", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1093": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1093, - "name": "Compartmentalization of GABAergic inhibition by dendritic spines (Chiu et al. 2013)", - "repository_type": "github", - "summary": "A spiny dendrite model supports the hypothesis that only inhibitory inputs on spine heads, not shafts, compartmentalizes inhibition of calcium signals to spine heads as seen in paired inhibition with back-propagating action potential experiments on prefrontal cortex layer 2/3 pyramidal neurons in mouse (Chiu et al. 2013).", - "tags": [ - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1604, - "tag": "ModelDB:143604" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:35:56.241383+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/143604", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1094": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1094, - "name": "Thalamic network model of deep brain stimulation in essential tremor (Birdno et al. 2012)", - "repository_type": "github", - "summary": "\"... Thus the decreased effectiveness\r\nof temporally irregular DBS trains is due to long pauses in the\r\nstimulus trains, not the degree of temporal irregularity alone.\r\n\r\nWe also\r\nconducted computer simulations of neuronal responses to the experimental\r\nstimulus trains using a biophysical model of the thalamic\r\nnetwork.\r\n\r\nTrains that suppressed tremor in volunteers also suppressed\r\nfluctuations in thalamic transmembrane potential at the frequency\r\nassociated with cerebellar burst-driver inputs.\r\n\r\nClinical and computational\r\nfindings indicate that DBS suppresses tremor by masking burst-driver\r\ninputs to the thalamus and that pauses in stimulation prevent\r\nsuch masking. Although stimulation of other anatomic targets may\r\nprovide tremor suppression, we propose that the most relevant neuronal\r\ntargets for effective tremor suppression are the afferent cerebellar\r\nfibers that terminate in the thalamus.\"\r\n", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 831, - "tag": "Deep brain stimulation" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1605, - "tag": "ModelDB:143633" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 746, - "tag": "Therapeutics" - } - ], - "timestamp_created": "2024-01-12 09:35:56.749067+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/143633", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1095": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1095, - "name": "Active dendritic action potential propagation (Casale & McCormick 2011)", - "repository_type": "github", - "summary": "This model explores the dendritic sodium and potassium conductances needed to recapitulate voltage-sensitive dye optical recordings of thalamic interneuron dendrites in the dorsal lateral geniculate nucleus. Model ion channels were selected based on pharmacological data.", - "tags": [ - { - "id": 1606, - "tag": "Conductance distributions" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 769, - "tag": "I_KHT" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1607, - "tag": "ModelDB:143635" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:35:57.265590+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/143635", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1096": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1096, - "name": "Simulations of motor unit discharge patterns (Powers et al. 2011)", - "repository_type": "github", - "summary": "\" ...\r\nTo estimate the potential contributions of PIC (Persistent Inward Current) activation and synaptic input patterns to\r\nmotor unit discharge patterns, we examined the responses of a set of cable\r\nmotoneuron models to different patterns of excitatory and inhibitory\r\ninputs.\r\n\r\nThe models were first tuned to approximate the current- and\r\nvoltage-clamp responses of low- and medium-threshold spinal motoneurons\r\nstudied in decerebrate cats and then driven with different patterns of\r\nexcitatory and inhibitory inputs.\r\n\r\nThe responses of the models to excitatory\r\ninputs reproduced a number of features of human motor unit\r\ndischarge.\r\n\r\nHowever, the pattern of rate modulation was strongly influenced\r\nby the temporal and spatial pattern of concurrent inhibitory inputs.\r\n\r\nThus, even though PIC activation is likely to exert a strong influence on\r\nfiring rate modulation, PIC activation in combination with different\r\npatterns of excitatory and inhibitory synaptic inputs can produce a wide\r\nvariety of motor unit discharge patterns.\"", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1608, - "tag": "ModelDB:143671" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:35:57.795181+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/143671", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1097": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1097, - "name": "CA1 pyramidal neuron: depolarization block (Bianchi et al. 2012)", - "repository_type": "github", - "summary": "NEURON files from the paper: On the mechanisms underlying the depolarization block in the spiking dynamics of CA1 pyramidal neurons\r\nby D.Bianchi, A. Marasco, A.Limongiello, C.Marchetti, H.Marie,B.Tirozzi, M.Migliore (2012). J Comput. Neurosci. In press. DOI: 10.1007/s10827-012-0383-y.\r\nExperimental findings shown that under sustained input current of increasing strength neurons eventually stop firing, entering a depolarization block.\r\nWe analyze the spiking dynamics of CA1 pyramidal neuron models using the same set of ionic currents on both an accurate morphological reconstruction and on its reduction to a single-compartment.\r\n The results show the specic ion channel properties and kinetics that are needed to\r\nreproduce the experimental findings, and how their interplay can drastically modulate the neuronal dynamics and the input current range leading to depolarization block.", - "tags": [ - { - "id": 804, - "tag": "Bifurcation" - }, - { - "id": 873, - "tag": "Depolarization block" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 1433, - "tag": "I_AHP" - }, - { - "id": 1609, - "tag": "Mathematica" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1610, - "tag": "ModelDB:143719" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 569, - "tag": "Simplified Models" - } - ], - "timestamp_created": "2024-01-12 09:35:58.398362+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/143719", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1098": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1098, - "name": "Inhibitory plasticity balances excitation and inhibition (Vogels et al. 2011)", - "repository_type": "github", - "summary": "\"Cortical neurons receive balanced excitatory and inhibitory synaptic currents.\r\n\r\nSuch a balance could be\r\nestablished and maintained in an experience-dependent manner by synaptic plasticity at inhibitory\r\nsynapses.\r\n\r\nWe show that this mechanism provides an explanation for the sparse firing patterns observed\r\nin response to natural stimuli and fits well with a recently observed interaction of excitatory and\r\ninhibitory receptive field plasticity.\r\n\r\n...\r\nOur results suggest an essential\r\nrole of inhibitory plasticity in the formation and maintenance of functional cortical circuitry.\"", - "tags": [ - { - "id": 645, - "tag": "Brian" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1611, - "tag": "ModelDB:143751" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 802, - "tag": "STDP" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - } - ], - "timestamp_created": "2024-01-12 09:35:58.988602+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/143751", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1099": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1099, - "name": "Competition model of pheromone ratio detection (Zavada et al. 2011)", - "repository_type": "github", - "summary": "For some closely related sympatric moth species, recognizing a specific pheromone component concentration ratio is essential for mating success. We propose and test a minimalist competition-based feed-forward neuronal model capable of detecting a certain ratio of pheromone components independently of overall concentration. This model represents an elementary recognition unit for binary mixtures which we propose is entirely contained in the macroglomerular complex (MGC) of the male moth. A set of such units, along with projection neurons (PNs), can provide the input to higher brain centres. We found that (1) accuracy is mainly achieved by maintaining a certain ratio of connection strengths between olfactory receptor neurons (ORN) and local neurons (LN), much less by properties of the interconnections between the competing LNs proper. (2) successful ratio recognition is achieved using latency-to-first-spike in the LN populations which. (3) longer durations of the competition process between LNs did not result in higher recognition accuracy.", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 1612, - "tag": "CNrun" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1613, - "tag": "ModelDB:143753" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 758, - "tag": "Sensory processing" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 542, - "tag": "neuroConstruct" - } - ], - "timestamp_created": "2024-01-12 09:35:59.545515+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/143753", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1100": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1100, - "name": "Point process framework for modeling electrical stimulation of auditory nerve (Goldwyn et al. 2012)", - "repository_type": "github", - "summary": "A point process model of the auditory nerve that provides a compact and accurate description of neural responses to electric stimulation. Inspired by the framework of generalized linear models, the model consists of a cascade of linear and nonlinear stages. A semi-analytical procedure uniquely determines each parameter in the model on the basis of fundamental statistics from recordings of single fiber responses to electric stimulation, including threshold, relative spread, jitter, and chronaxie. The model also accounts for refractory and summation effects that influence the responses of auditory nerve fibers to high pulse rate stimulation.", - "tags": [ - { - "id": 762, - "tag": "Audition" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1614, - "tag": "ModelDB:143760" - } - ], - "timestamp_created": "2024-01-12 09:36:00.118113+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/143760", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1101": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1101, - "name": "Grid cell spatial firing models (Zilli 2012)", - "repository_type": "github", - "summary": "This package contains MATLAB implementations of most models (published from 2005 to 2011) of the hexagonal firing field arrangement of grid cells.", - "tags": [ - { - "id": 718, - "tag": "Attractor Neural Network" - }, - { - "id": 1461, - "tag": "Grid cell" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1615, - "tag": "ModelDB:144006" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 800, - "tag": "Spatial Navigation" - } - ], - "timestamp_created": "2024-01-12 09:36:00.680413+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144006", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1102": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1102, - "name": "LGMD Variability and logarithmic compression in dendrites (Jones and Gabbiani, 2012, 2012B)", - "repository_type": "github", - "summary": "A compartmental model of the LGMD with a simplified, rake shaped, excitatory dendrite. It receives spontaneous input and excitatory and inhibitory synaptic inputs triggered by visual stimuli. It generates realistic responses to looming through the velocity dependent scaling and delay of individual excitatory synaptic inputs, with variability. We use the model to show that the key determinants of output variability are spontaneous input and temporal jitter of the excitatory inputs, rather than variability in magnitude of individual inputs (2012B, J Neurophysiol). We also use the model to analyze the transformation of the excitatory signals through the visual pathway; concluding that the representation of stimulus velocity is transformed from an expansive relationship at the level of the LGMD inputs to a logarithmic one at the level of its membrane potential (2012, J Neurosci).\r\n", - "tags": [ - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1616, - "tag": "ModelDB:144007" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 758, - "tag": "Sensory processing" - }, - { - "id": 726, - "tag": "Vision" - } - ], - "timestamp_created": "2024-01-12 09:36:01.262410+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144007", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1103": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1103, - "name": "Epileptic seizure model with Morris-Lecar neurons (Beverlin and Netoff 2011)", - "repository_type": "github", - "summary": "Here we use phase-response curves (PRC) from Morris-Lecar (M-L) model neurons with synaptic depression and gradually decrease input current to cells within a network simulation. This method effectively decreases firing rates resulting in a shift to greater network synchrony illustrating a possible mechanism of the transition phenomenon. PRCs are measured from the M-L conductance based model cell with a range of input currents within the limit cycle. A large network of 3000 excitatory neurons is simulated with a network topology generated from second-order statistics which allows a range of population synchrony. The population synchrony of the oscillating cells is measured with the Kuramoto order parameter, which reveals a transition from tonic to clonic phase exhibited by our model network.", - "tags": [ - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1617, - "tag": "ModelDB:144010" - } - ], - "timestamp_created": "2024-01-12 09:36:02.098213+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144010", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1104": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1104, - "name": "Perturbation sensitivity implies high noise and suggests rate coding in cortex (London et al. 2010)", - "repository_type": "github", - "summary": "\"... The network simulations\r\nwere also based on a previously published model(Latham et al. 2000), but with modifications to\r\nallow the addition and detection of extra spikes (see Supplementary Information,\r\nsection 7).\"", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1618, - "tag": "ModelDB:144027" - } - ], - "timestamp_created": "2024-01-12 09:36:02.618150+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144027", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1105": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1105, - "name": "Synchrony by synapse location (McTavish et al. 2012)", - "repository_type": "github", - "summary": "This model considers synchrony between mitral cells induced via shared\r\ngranule cell interneurons while taking into account the spatial\r\nconstraints of the system. In particular, since inhibitory inputs\r\ndecay passively along the lateral dendrites, this model demonstrates\r\nthat an optimal arrangement of the inhibitory synapses will be near\r\nthe cell bodies of the relevant mitral cells.", - "tags": [ - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1619, - "tag": "ModelDB:144054" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 586, - "tag": "Synchronization" - } - ], - "timestamp_created": "2024-01-12 09:36:03.109476+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144054", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1106": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1106, - "name": "Layer V PFC pyramidal neuron used to study persistent activity (Sidiropoulou & Poirazi 2012)", - "repository_type": "github", - "summary": "\"... Here, we use a compartmental modeling approach to search for discriminatory features in the properties of incoming stimuli to a PFC pyramidal neuron and/or its response that signal which of these stimuli will result in persistent activity emergence. \r\n\r\nFurthermore, we use our modeling approach to study cell-type specific differences in persistent activity properties, via implementing a regular spiking (RS) and an intrinsic bursting (IB) model neuron. \r\n...\r\nCollectively, our results pinpoint to specific features of the neuronal response to a given stimulus that code for its ability to induce persistent activity and predict differential roles of RS and IB neurons in persistent activity expression.\r\n\"", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 731, - "tag": "I CAN" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1620, - "tag": "ModelDB:144089" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:36:03.614270+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144089", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1107": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1107, - "name": "Surround Suppression in V1 via Withdraw of Balanced Local Excitation in V1 (Shushruth 2012)", - "repository_type": "github", - "summary": "The model is mean-field network models, which is set up as a so-called ring-model, i. e. it is a highly idealized model of an orientation hypercolumn in primary visual cortex. Long-range intra-areal and inter-areal feedback connections are modeled phenomenologically as an external input. In this model, there are recurrent interactions via short-range local connections between orientation columns, but not between hypercolumns.", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1621, - "tag": "ModelDB:144096" - }, - { - "id": 726, - "tag": "Vision" - } - ], - "timestamp_created": "2024-01-12 09:36:04.213666+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144096", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1108": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1108, - "name": "Cortical pyramidal neuron, phase response curve (Stiefel et al 2009)", - "repository_type": "github", - "summary": "Three models of increasing complexity all showing a switch from type II (biphasic) to type I (monophasic) phase response curves with a cholinergic down-modulation of K+ conductances.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1622, - "tag": "ModelDB:144372" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:36:04.716920+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144372", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1109": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1109, - "name": "Hodgkin-Huxley model of persistent activity in PFC neurons (Winograd et al. 2008) (NEURON python)", - "repository_type": "github", - "summary": "The paper demonstrate a form of graded persistent activity activated by hyperpolarization. This phenomenon is modeled based on a slow calcium regulation of Ih, similar to that introduced\r\nearlier for thalamic neurons (see Destexhe et al., J Neurophysiol. 1996). The only difference is that the calcium signal is here provided by the high-threshold calcium current (instead of the low-threshold calcium current in thalamic neurons).", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1623, - "tag": "ModelDB:144376" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:36:05.275426+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144376", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1110": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1110, - "name": "A multiphysics neuron model for cellular volume dynamics (Lee et al. 2011)", - "repository_type": "github", - "summary": "This paper introduces a novel neuron model, where the cell volume is a time-varying variable and multiple physical principles are combined to build governing equations. Using this model, we analyzed neuronal volume responses during excitation, which elucidated the waveforms of fast intrinsic optical signals observed experimentally across the literature. In addition, we analyzed volume responses on a longer time scale with repetitive stimulation to study the characteristics of slow cell swelling.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 1624, - "tag": "Cellular volume dynamics" - }, - { - "id": 1531, - "tag": "I Cl, leak" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 861, - "tag": "I_K,Na" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1625, - "tag": "ModelDB:144380" - }, - { - "id": 740, - "tag": "Na/K pump" - }, - { - "id": 1626, - "tag": "Osmosis-driven water flux" - } - ], - "timestamp_created": "2024-01-12 09:36:06.049401+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144380", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1111": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1111, - "name": "Dendro-dendritic synaptic circuit (Shepherd Brayton 1979)", - "repository_type": "github", - "summary": "A NEURON simulation has been created to model the passive spread of an EPSP from a mitral cell synapse on a granule cell spine. The EPSP was shown to propagate subthreshold through the dendritic shaft into an adjacent spine with significant amplitude (figure 2B).", - "tags": [ - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1627, - "tag": "ModelDB:144385" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - } - ], - "timestamp_created": "2024-01-12 09:36:06.548526+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144385", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1112": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 1112, - "name": "Lobster STG pyloric network model with calcium sensor (Gunay & Prinz 2010) (Prinz et al. 2004)", - "repository_type": "github", - "summary": "This pyloric network model simulator is a C/C++ program that saves 384 different calcium sensor values that are candidates for activity sensors (Gunay and Prinz, 2010). The simulator was used to scan all of the 20 million pyloric network models that were previously collected in a database (Prinz et al, 2004).", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 776, - "tag": "C or Cplusplus program (web link to model)" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 75, - "tag": "Homeostasis" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1628, - "tag": "ModelDB:144387" - } - ], - "timestamp_created": "2024-01-12 09:36:07.064363+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144387", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1113": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1113, - "name": "CA1 pyramidal neurons: effects of Kv7 (M-) channels on synaptic integration (Shah et al. 2011)", - "repository_type": "github", - "summary": "NEURON mod files from the paper:\r\nShah et al., 2011.\r\n\r\nIn this study, using a combination of electrophysiology\r\nand computational modelling, we show that these channels selectively influence peri-somatic but not dendritic post-synaptic excitatory synaptic potential (EPSP) integration in CA1 pyramidal cells. This may be important for their relative contributions to physiological processes such as synaptic plasticity as well as patho-physiological conditions such as epilepsy.", - "tags": [ - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 1433, - "tag": "I_AHP" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1629, - "tag": "ModelDB:144392" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 596, - "tag": "Synaptic Integration" - } - ], - "timestamp_created": "2024-01-12 09:36:07.577508+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144392", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1114": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1114, - "name": "CA1 pyramidal cell: reconstructed axonal arbor and failures at weak gap junctions (Vladimirov 2011)", - "repository_type": "github", - "summary": "Model of pyramidal CA1 cells connected by gap junctions in their axons.\r\nCell geometry is based on anatomical reconstruction of rat CA1 cell (NeuroMorpho.Org ID: NMO_00927) with long axonal arbor.\r\n\r\nModel init_2cells.hoc shows failures of second spike propagation in a spike doublet, depending on conductance of an axonal gap junction.\r\n\r\nModel init_ring.hoc shows that spike failure result in reentrant oscillations of a spike in a loop of axons connected by gap junctions, where one gap junction is weak.\r\n\r\nThe paper shows that in random networks of axons connected by gap junctions, oscillations are driven by single pacemaker loop of axons. The shortest loop, around which a spike can travel, is the most likely pacemaker.\r\nThis principle allows us to predict the frequency of oscillations from network connectivity and visa versa. We propose that this type of oscillations corresponds to so-called fast ripples in epileptic hippocampus.", - "tags": [ - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 745, - "tag": "Conduction failure" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1630, - "tag": "ModelDB:144401" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - } - ], - "timestamp_created": "2024-01-12 09:36:08.095133+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144401", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1115": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1115, - "name": "A Moth MGC Model-A HH network with quantitative rate reduction (Buckley & Nowotny 2011)", - "repository_type": "github", - "summary": "We provide the model used in Buckley & Nowotny (2011). It consists of a network of Hodgkin Huxley neurons coupled by slow GABA_B synapses which is run alongside a quantitative reduction described in the associated paper.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 804, - "tag": "Bifurcation" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 1532, - "tag": "I Na, leak" - }, - { - "id": 843, - "tag": "I Q" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1631, - "tag": "ModelDB:144403" - }, - { - "id": 866, - "tag": "Multiscale" - } - ], - "timestamp_created": "2024-01-12 09:36:08.772458+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144403", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1116": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1116, - "name": "Cerebellar gain and timing control model (Yamazaki & Tanaka 2007)(Yamazaki & Nagao 2012)", - "repository_type": "github", - "summary": "This paper proposes a hypothetical computational mechanism for unified gain and timing control in the cerebellum. The hypothesis is justified by computer simulations of a large-scale spiking network model of the cerebellum.", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 825, - "tag": "Learning" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1632, - "tag": "ModelDB:144416" - }, - { - "id": 758, - "tag": "Sensory processing" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - } - ], - "timestamp_created": "2024-01-12 09:36:09.350427+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144416", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1117": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1117, - "name": "Linear vs non-linear integration in CA1 oblique dendrites (G\u00f3mez Gonz\u00e1lez et al. 2011)", - "repository_type": "github", - "summary": "The hippocampus in well known for its role in learning and memory processes. The CA1 region is the output of the hippocampal formation and pyramidal neurons in this region are the elementary units responsible for the processing and transfer of information to the cortex. Using this detailed single neuron model, it is investigated the conditions under which individual CA1 pyramidal neurons process incoming information in a complex (non-linear) as opposed to a passive (linear) manner.\r\nThis detailed compartmental model of a CA1 pyramidal neuron is based on one described previously (Poirazi, 2003). The model was adapted to five different reconstructed morphologies for this study, and slightly modified to fit the experimental data of (Losonczy, 2006), and to incorporate evidence in pyramidal neurons for the non-saturation of NMDA receptor-mediated conductances by single glutamate pulses. We first replicate the main findings of (Losonczy, 2006), including the very brief window for nonlinear integration using single-pulse stimuli. We then show that double-pulse stimuli increase a CA1 pyramidal neuron\u2019s tolerance for input asynchrony by at last an order of magnitude. Therefore, it is shown using this model, that the time window for nonlinear integration is extended by more than an order of magnitude when inputs are short bursts as opposed to single spikes.\r\n", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 731, - "tag": "I CAN" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 1433, - "tag": "I_AHP" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1633, - "tag": "ModelDB:144450" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 596, - "tag": "Synaptic Integration" - } - ], - "timestamp_created": "2024-01-12 09:36:09.857361+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144450", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1118": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1118, - "name": "Connection-set Algebra (CSA) for the representation of connectivity in NN models (Djurfeldt 2012)", - "repository_type": "github", - "summary": "\"The connection-set algebra (CSA) is a novel\r\nand general formalism for the description of connectivity\r\nin neuronal network models, from small-scale to\r\nlarge-scale structure. ... The expressiveness of CSA\r\nmakes prototyping of network structure easy. A C++\r\nversion of the algebra has been implemented and used\r\nin a large-scale neuronal network simulation (Djurfeldt\r\net al., IBM J Res Dev 52(1/2):31\u201342, 2008b) and an\r\nimplementation in Python has been publicly released.\"", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1634, - "tag": "ModelDB:144455" - }, - { - "id": 620, - "tag": "Python" - } - ], - "timestamp_created": "2024-01-12 09:36:10.365976+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144455", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1119": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1119, - "name": "CA1 pyramidal neuron dendritic spine with plasticity (O`Donnell et al. 2011)", - "repository_type": "github", - "summary": "Biophysical model of a dendritic spine and adjacent dendrite with synapse. Model parameters adjusted to fit CA3-CA1 Shaffer collateral synapse data from literature. Model includes both electrical and Ca2+ dynamics, including AMPARs, NMDARs, 4 types of CaV channel, and leak conductance. Spine and synapse are plastic according to Ca2+ dependent rule.\r\n\r\nThe aim of the model is to explore the effects of dendritic spine structural plasticity on the rules of synaptic plasticity.", - "tags": [ - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1635, - "tag": "ModelDB:144463" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - } - ], - "timestamp_created": "2024-01-12 09:36:10.879597+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144463", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1120": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1120, - "name": "Markov Chain-based Stochastic Shielding Hodgkin Huxley Model (Schmandt, Galan 2012)", - "repository_type": "github", - "summary": "Markov Chain-based Stochastic Shielding Hodgkin Huxley Model (Schmandt, Galan 2012)", - "tags": [ - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1636, - "tag": "ModelDB:144468" - } - ], - "timestamp_created": "2024-01-12 09:39:32.390588+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144468", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1121": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1121, - "name": "Functional consequences of cortical circuit abnormalities on gamma in schizophrenia (Spencer 2009)", - "repository_type": "github", - "summary": "\"Schizophrenia is characterized by cortical circuit abnormalities, which might be reflected in\r\ngamma-frequency (30\u2013100 Hz) oscillations in the electroencephalogram. Here we used a computational\r\nmodel of cortical circuitry to examine the effects that neural circuit abnormalities might have\r\non gamma generation and network excitability. The model network consisted of 1000 leaky integrateand-\r\nfi re neurons with realistic connectivity patterns and proportions of neuron types [pyramidal\r\ncells (PCs), regular-spiking inhibitory interneurons, and fast-spiking interneurons (FSIs)].\r\n...\r\nThe results of this study suggest that a multimodal\r\napproach, combining non-invasive neurophysiological and structural measures, might be\r\nable to distinguish between different neural circuit abnormalities in schizophrenia patients.\r\n...\"\r\n", - "tags": [ - { - "id": 1527, - "tag": "Brain Rhythms" - }, - { - "id": 1637, - "tag": "IDL" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1638, - "tag": "ModelDB:144477" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 845, - "tag": "Schizophrenia" - }, - { - "id": 569, - "tag": "Simplified Models" - } - ], - "timestamp_created": "2024-01-12 09:39:33.024284+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144477", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1122": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1122, - "name": "Nodes of Ranvier with left-shifted Nav channels (Boucher et al. 2012)", - "repository_type": "github", - "summary": "The two programs CLSRanvier.f and propagation.f simulate the excitability of a myelinated axon with injured nodes of Ranvier.\r\nThe injury is simulated as the Coupled Left Shift (CLS) of the activation(V) and inactivation(V) (availability) of a fraction of Nav channels.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 745, - "tag": "Conduction failure" - }, - { - "id": 873, - "tag": "Depolarization block" - }, - { - "id": 710, - "tag": "FORTRAN" - }, - { - "id": 75, - "tag": "Homeostasis" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 1532, - "tag": "I Na, leak" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1639, - "tag": "ModelDB:144481" - }, - { - "id": 740, - "tag": "Na/K pump" - }, - { - "id": 787, - "tag": "Pathophysiology" - }, - { - "id": 741, - "tag": "Sodium pump" - } - ], - "timestamp_created": "2024-01-12 09:39:33.639779+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144481", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1123": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1123, - "name": "Pyramidal neuron conductances state and STDP (Delgado et al. 2010)", - "repository_type": "github", - "summary": "Neocortical neurons in vivo process each of their individual inputs in the context of ongoing synaptic background activity, produced by the thousands of presynaptic partners a typical neuron has. That background activity affects multiple aspects of neuronal and network function. However, its effect on the induction of spike-timing dependent plasticity (STDP) is not clear.\r\nUsing the present biophysically-detailed computational model, it is not only able to replicate the conductance-dependent shunting of dendritic potentials (Delgado et al,2010), but show that synaptic background can truncate calcium dynamics within dendritic spines, in a way that affects potentiation more strongly than depression.\r\nThis program uses a simplified layer 2/3 pyramidal neuron constructed in NEURON.\r\nIt was similar to the model of Traub et al., J Neurophysiol. (2003), and consisted of a soma, an apical shaft, distal dendrites, five basal dendrites, an axon, and a single spine. The spine\u2019s location was variable along the apical shaft (initial 50 μm) and apical. The axon contained an axon hillock region, an initial segment, segments with myelin, and nodes of Ranvier, in order to have realistic action potential generation. For more information about the model see supplemental material, Delgado et al 2010.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 762, - "tag": "Audition" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 1606, - "tag": "Conductance distributions" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 1433, - "tag": "I_AHP" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1640, - "tag": "ModelDB:144482" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 802, - "tag": "STDP" - } - ], - "timestamp_created": "2024-01-12 09:39:34.164589+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144482", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1124": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1124, - "name": "Reflected SDE Hodgkin-Huxley Model (Dangerfield et al. 2012)", - "repository_type": "github", - "summary": "Matlab code for simulating channel noise using the original Hodgkin-Huxley equations and a variant of the Hodkgin-Huxley model from (Bruce, Annals Bio Eng, Vol 36, pp 824-838, 2009). Methods used in simulation are SSA, SDE method and RSDE method.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1641, - "tag": "ModelDB:144489" - } - ], - "timestamp_created": "2024-01-12 09:39:34.706979+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144489", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1125": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1125, - "name": "CA1 pyramidal neuron: synaptically-induced bAP predicts synapse location (Sterratt et al. 2012)", - "repository_type": "github", - "summary": "This is an adaptation of Poirazi et al.'s (2003) CA1 model that is used to measure BAP-induced voltage and calcium signals in spines after simulated Schaffer\r\ncollateral synapse stimulation. In the model, the peak calcium concentration is highly\r\ncorrelated with soma-synapse distance under a number of physiologically-realistic\r\nsuprathreshold stimulation regimes and for a range of dendritic morphologies. There are also simulations demonstrating that peak calcium can be used to set up a synaptic democracy\r\nin a homeostatic manner, whereby synapses regulate their synaptic strength on the\r\nbasis of the difference between peak calcium and a uniform target value.", - "tags": [ - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 805, - "tag": "I Mixed" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 844, - "tag": "I R" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 1433, - "tag": "I_AHP" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1642, - "tag": "ModelDB:144490" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - } - ], - "timestamp_created": "2024-01-12 09:39:35.221601+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144490", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1126": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1126, - "name": "Stochastic versions of the Hodgkin-Huxley equations (Goldwyn, Shea-Brown 2011) (pylab)", - "repository_type": "github", - "summary": "A pylab version from Alan Leggitt for simulating different channel noise models using the Hodgkin-Huxley equations. Methods provided and reviewed in Goldwyn and Shea-Brown (2011) are: current noise, subunit noise, conductance noise, and Markov chain, as well as the standard deterministic Hodgkin-Huxley model.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 748, - "tag": "Markov-type model" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1643, - "tag": "ModelDB:144499" - }, - { - "id": 620, - "tag": "Python" - } - ], - "timestamp_created": "2024-01-12 09:39:35.749349+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144499", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1127": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1127, - "name": "Inferior Olive, subthreshold oscillations (Torben-Nielsen, Segev, Yarom 2012)", - "repository_type": "github", - "summary": "The Inferior Olive is a brain structure in which neurons are solely connected to each other through gap-junctions. Its behavior is characterized by spontaneous subthreshold oscillation, frequency changes in the subthreshold oscillation, stable phase differences between neurons, and propagating waves of activity.\r\n\r\nOur model based on actual IO topology can reproduce these behaviors and provides a mechanistic explanation thereof.", - "tags": [ - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1644, - "tag": "ModelDB:144502" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - } - ], - "timestamp_created": "2024-01-12 09:39:36.279155+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144502", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1128": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1128, - "name": "Duration-tuned neurons from the inferior colliculus of the big brown bat (Aubie et al. 2009)", - "repository_type": "github", - "summary": "dtnet is a generalized neural network simulator written in C++ with an easy to use XML description language to generate arbitrary neural networks and then run simulations covering many different parameter values. For example, you can specify ranges of parameter values for several different connection weights and then automatically run simulations over all possible parameters. Graphing ability is built in as long as the free, open-source, graphing application GLE (http://glx.sourceforge.net/) is installed.\r\n\r\nIncluded in the examples folder are simulation descriptions that were used to generate the results in Aubie et al. (2009). Refer to the README file for instructions on compiling and running these examples.\r\n\r\nThe most recent source code can be obtained from GitHub: https://github.com/baubie/dtnet\r\n", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 595, - "tag": "Coincidence Detection" - }, - { - "id": 754, - "tag": "Delay" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1645, - "tag": "ModelDB:144509" - }, - { - "id": 573, - "tag": "Rebound firing" - }, - { - "id": 569, - "tag": "Simplified Models" - } - ], - "timestamp_created": "2024-01-12 09:39:36.773134+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144509", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1129": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1129, - "name": "Duration-tuned neurons from the inferior colliculus of vertebrates (Aubie et al. 2012)", - "repository_type": "github", - "summary": "These models reproduce the responses of duration-tuned neurons in the auditory midbrain of the big brown bat, the rat, the mouse and the frog (Aubie et al. 2012). They are written in the Python interface to NEURON and a subset of the figures from Aubie et al. (2012) are pre-set in run.py (raw data is generated and a separate graphing program must be used to visualize the results).", - "tags": [ - { - "id": 762, - "tag": "Audition" - }, - { - "id": 595, - "tag": "Coincidence Detection" - }, - { - "id": 1646, - "tag": "Duration Selectivity" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1647, - "tag": "ModelDB:144511" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 758, - "tag": "Sensory processing" - }, - { - "id": 569, - "tag": "Simplified Models" - } - ], - "timestamp_created": "2024-01-12 09:39:37.299339+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144511", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1130": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 1130, - "name": "Half-center oscillator database of leech heart interneuron model (Doloc-Mihu & Calabrese 2011)", - "repository_type": "github", - "summary": "We have created a database (HCO-db) of instances of a half-center oscillator computational model [Hill et al., 2001] for analyzing how neuronal parameters influence network activity. We systematically explored the parameter space of about 10.4 million simulated HCO instances and corresponding isolated neuron model simulations obtained by varying a set of selected parameters (maximal conductance of intrinsic and synaptic currents) in all combinations using a brute-force approach. We classified these HCO instances by their activity characteristics into identifiable groups. We built an efficient relational database table (HCO-db) with the resulting instances characteristics.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 729, - "tag": "Invertebrate" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1648, - "tag": "ModelDB:144518" - }, - { - "id": 1649, - "tag": "MySQL (web link to model)" - }, - { - "id": 585, - "tag": "Oscillations" - } - ], - "timestamp_created": "2024-01-12 09:39:37.851607+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144518", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1131": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1131, - "name": "Cardiac action potentials and pacemaker activity of sinoatrial node (DiFrancesco & Noble 1985)", - "repository_type": "github", - "summary": "\"Equations have been developed to describe cardiac action potentials and pacemaker activity. The model takes account of extensive developments in experimental work ...\"", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1650, - "tag": "ModelDB:144520" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 738, - "tag": "Na/Ca exchanger" - }, - { - "id": 740, - "tag": "Na/K pump" - } - ], - "timestamp_created": "2024-01-12 09:39:38.383877+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144520", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1132": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1132, - "name": "STD-dependent and independent encoding of Input irregularity as spike rate (Luthman et al. 2011)", - "repository_type": "github", - "summary": "\"... We use a\r\nconductance-based model of a CN neuron to study the\r\neffect of the regularity of Purkinje cell spiking on CN\r\nneuron activity.\r\n\r\nWe find that increasing the irregularity of\r\nPurkinje cell activity accelerates the CN neuron spike rate\r\nand that the mechanism of this recoding of input irregularity\r\nas output spike rate depends on the number of Purkinje\r\ncells converging onto a CN neuron.\r\n...\"\r\n", - "tags": [ - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1651, - "tag": "ModelDB:144523" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - } - ], - "timestamp_created": "2024-01-12 09:39:38.964969+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144523", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1133": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1133, - "name": "State and location dependence of action potential metabolic cost (Hallermann et al., 2012)", - "repository_type": "github", - "summary": "With this model of a layer 5 pyramidal neuron the state and location dependence of the ATP usage and the metabolic efficiency of action potentials can be analyzed. Model parameters were constrained by direct subcellular recordings at dendritic, somatic and axonal compartments.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1652, - "tag": "ModelDB:144526" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:39:39.561309+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144526", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1134": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1134, - "name": "Reinforcement learning of targeted movement (Chadderdon et al. 2012)", - "repository_type": "github", - "summary": "\"Sensorimotor control has traditionally been considered from a control theory perspective, without relation to neurobiology. In contrast, here we utilized a spiking-neuron model of motor cortex and trained it to perform a simple movement task, which consisted of rotating a single-joint \u201cforearm\u201d to a target. Learning was based on a reinforcement mechanism analogous to that of the dopamine system. This provided a global reward or punishment signal in response to decreasing or increasing distance from hand to target, respectively. Output was partially driven by Poisson motor babbling, creating stochastic movements that could then be shaped by learning. The virtual forearm consisted of a single segment rotated around an elbow joint, controlled by flexor and extensor muscles. ...\"", - "tags": [ - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1653, - "tag": "ModelDB:144538" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 809, - "tag": "Reinforcement Learning" - }, - { - "id": 859, - "tag": "Reward-modulated STDP" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - } - ], - "timestamp_created": "2024-01-12 09:39:40.270090+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144538", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1135": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1135, - "name": "CA1 pyramidal neuron: Ih current (Migliore et al. 2012)", - "repository_type": "github", - "summary": "NEURON files from the paper:\r\nMigliore M, Migliore R (2012) Know Your Current Ih: Interaction with a Shunting Current Explains the Puzzling Effects of Its Pharmacological or\r\nPathological Modulations. PLoS ONE 7(5): e36867.\r\ndoi:10.1371/journal.pone.0036867.\r\n\r\n\r\nExperimental findings on the effects of Ih current modulation, which is particularly involved in epilepsy, appear to be inconsistent. In the paper, using a realistic model we show how and why a shunting current, such as that carried by TASK-like channels, dependent on the Ih peak conductance is able to explain virtually all experimental findings on Ih up- or down-regulation by modulators or pathological conditions.\r\n", - "tags": [ - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1654, - "tag": "ModelDB:144541" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 596, - "tag": "Synaptic Integration" - } - ], - "timestamp_created": "2024-01-12 09:39:40.904809+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144541", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1136": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1136, - "name": "Hopfield and Brody model (Hopfield, Brody 2000) (NEURON+python)", - "repository_type": "github", - "summary": "Demonstration of Hopfield-Brody snychronization using artificial cells in NEURON+python.", - "tags": [ - { - "id": 718, - "tag": "Attractor Neural Network" - }, - { - "id": 595, - "tag": "Coincidence Detection" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1655, - "tag": "ModelDB:144549" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 719, - "tag": "Pattern Recognition" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - } - ], - "timestamp_created": "2024-01-12 09:39:41.514973+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144549", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1137": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1137, - "name": "Rhesus Monkey Layer 3 Pyramidal Neurons: V1 vs PFC (Amatrudo, Weaver et al. 2012)", - "repository_type": "github", - "summary": "Whole-cell patch-clamp recordings and high-resolution 3D morphometric analyses of layer 3 pyramidal neurons in in vitro slices of monkey primary visual cortex (V1) and dorsolateral granular prefrontal cortex (dlPFC) revealed that neurons in these two brain areas possess highly distinctive structural and functional properties. ... Three-dimensional reconstructions of V1 and dlPFC neurons were incorporated into computational models containing Hodgkin-Huxley and AMPA- and GABAA-receptor gated channels. Morphology alone largely accounted for observed passive physiological properties, but led to AP firing rates that differed more than observed empirically, and to synaptic responses that opposed empirical results. Accordingly, modeling predicts that active channel conductances differ between V1 and dlPFC neurons. The unique features of V1 and dlPFC neurons are likely fundamental\r\ndeterminants of area-specific network behavior. The compact electrotonic arbor and increased excitability of V1 neurons support the rapid signal integration required for early processing of visual information. The greater connectivity and dendritic complexity of dlPFC neurons likely\r\nsupport higher level cognitive functions including working memory and planning.", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 1606, - "tag": "Conductance distributions" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 786, - "tag": "Electrotonus" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1656, - "tag": "ModelDB:144553" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 726, - "tag": "Vision" - } - ], - "timestamp_created": "2024-01-12 09:39:42.069629+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144553", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1138": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1138, - "name": "Computing with neural synchrony (Brette 2012)", - "repository_type": "github", - "summary": "\"... In a heterogeneous neural population, it appears that synchrony patterns represent structure or sensory invariants in stimuli, which can then be detected by postsynaptic neurons. The required neural circuitry can spontaneously emerge with spike-timing-dependent plasticity. Using examples in different sensory modalities, I show that this allows simple neural circuits to extract relevant information from realistic sensory stimuli, for example to identify a fluctuating odor in the presence of distractors. ...\"", - "tags": [ - { - "id": 645, - "tag": "Brian" - }, - { - "id": 75, - "tag": "Homeostasis" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1657, - "tag": "ModelDB:144560" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 573, - "tag": "Rebound firing" - }, - { - "id": 862, - "tag": "Reliability" - }, - { - "id": 802, - "tag": "STDP" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 586, - "tag": "Synchronization" - } - ], - "timestamp_created": "2024-01-12 09:39:42.578429+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144560", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1139": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1139, - "name": "Voltage-based STDP synapse (Clopath et al. 2010)", - "repository_type": "github", - "summary": "Implementation of the STDP rule by Clopath et al., Nat. Neurosci. 13(3):344-352,2010\r\nSTDP mechanism added to the AlphaSynapse in NEURON.", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1658, - "tag": "ModelDB:144566" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 802, - "tag": "STDP" - } - ], - "timestamp_created": "2024-01-12 09:39:43.408703+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144566", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1140": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1140, - "name": "Large scale model of the olfactory bulb (Yu et al., 2013)", - "repository_type": "github", - "summary": "The readme file currently contains links to the results for all the 72 odors investigated in the paper, and the movie showing the network activity during learning of odor k3-3 (an aliphatic ketone).\r\n", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1659, - "tag": "ModelDB:144570" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 719, - "tag": "Pattern Recognition" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 803, - "tag": "Unsupervised Learning" - } - ], - "timestamp_created": "2024-01-12 09:39:43.991915+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144570", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1141": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1141, - "name": "Thalamic neuron, zebra finch DLM: Integration of pallidal and cortical inputs (Goldberg et al. 2012)", - "repository_type": "github", - "summary": "This is a single-compartment model of a zebra finch thalamic relay neuron from nucleus DLM. It is used to explore the interaction between cortex-like glutamatergic input and pallidum-like GABAergic input as they control the spiking output of these neurons.", - "tags": [ - { - "id": 1609, - "tag": "Mathematica" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1660, - "tag": "ModelDB:144572" - }, - { - "id": 573, - "tag": "Rebound firing" - }, - { - "id": 596, - "tag": "Synaptic Integration" - } - ], - "timestamp_created": "2024-01-12 09:39:44.541948+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144572", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1142": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1142, - "name": "Parametric computation and persistent gamma in a cortical model (Chambers et al. 2012)", - "repository_type": "github", - "summary": "Using the Traub et al (2005) model of the cortex we determined how 33 synaptic strength parameters control gamma oscillations. We used fractional factorial design to reduce the number of runs required to 4096. We found an expected multiplicative interaction between parameters.", - "tags": [ - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 861, - "tag": "I_K,Na" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1661, - "tag": "ModelDB:144579" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 863, - "tag": "Parameter sensitivity" - } - ], - "timestamp_created": "2024-01-12 09:39:45.058119+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144579", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1143": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1143, - "name": "Boolean network-based analysis of the apoptosis network (Mai and Liu 2009)", - "repository_type": "github", - "summary": "\"To understand the design principles of the molecular interaction network associated with the\r\nirreversibility of cell apoptosis and the stability of cell surviving, we constructed a Boolean network integrating both the intrinsic and extrinsic pro-apoptotic pathways with pro-survival signal transduction pathways.\r\nWe performed statistical analyses of the dependences of cell fate on initial\r\nstates and on input signals.\r\nThe analyses reproduced the well-known pro- and anti-apoptotic effects of\r\nkey external signals and network components. We found that the external GF signal by itself did not change the apoptotic ratio from randomly chosen initial states when there is no external TNF signal, but can significantly offset apoptosis induced by the TNF signal. ...\"", - "tags": [ - { - "id": 1662, - "tag": "Apoptosis" - }, - { - "id": 1663, - "tag": "Boolean network" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1664, - "tag": "ModelDB:144586" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 751, - "tag": "Signaling pathways" - } - ], - "timestamp_created": "2024-01-12 09:39:45.575666+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144586", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1144": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1144, - "name": "Modulation of hippocampal rhythms by electric fields and network topology (Berzhanskaya et al. 2013)", - "repository_type": "github", - "summary": "\u201c\u2026 Here we present experimental and computational evidence of the interplay among hippocampal synaptic circuitry, neuronal morphology, external electric fields, and network activity.\r\n\r\nElectrophysiological data are used to constrain and validate an anatomically and biophysically realistic model of area CA1 containing pyramidal cells and two interneuron types: dendritic- and perisomatic-targeting.\r\n\r\nWe report two lines of results: addressing the network structure capable of generating theta-modulated gamma rhythms, and demonstrating electric field effects on those rhythms. First, theta-modulated gamma rhythms require specific inhibitory connectivity.\r\n\r\n\u2026\r\n\r\nThe second major finding is that subthreshold electric fields robustly alter the balance between different rhythms.\r\n\r\n\u2026\u201d", - "tags": [ - { - "id": 1527, - "tag": "Brain Rhythms" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1665, - "tag": "ModelDB:144589" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:39:46.107602+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144589", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1145": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1145, - "name": "Network model with dynamic ion concentrations (Ullah et al. 2009)", - "repository_type": "github", - "summary": "This is a network model composed of 100 excitatory and 100 inhibitory neurons with dynamic ion concentrations as described in \"The Influence of Sodium and Potassium Dynamics on Excitability, Seizures, and the Stability of Persistent States: II. Network and Glia Dynamics (2009) Journal of Computational Neuroscience, 26:171-183\".", - "tags": [ - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 710, - "tag": "FORTRAN" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1666, - "tag": "ModelDB:144590" - } - ], - "timestamp_created": "2024-01-12 09:39:46.615752+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144590", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1146": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1146, - "name": "CA1 pyramidal neurons: effects of Alzheimer (Culmone and Migliore 2012)", - "repository_type": "github", - "summary": "The model predicts possible therapeutic treatments of Alzheimers's Disease in terms of pharmacological manipulations of channels' kinetic and activation properties. The results suggest how and which mechanism can be targeted by a drug to restore the original firing conditions. The simulations reproduce somatic membrane potential in control conditions, when 90% of membrane is affected by AD (Fig.4A of the paper), and after treatment (Fig.4B of the paper).\r\n", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 827, - "tag": "Aging/Alzheimer`s" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1667, - "tag": "ModelDB:144976" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:39:47.118727+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144976", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1147": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1147, - "name": "Method for deriving general HH neuron model`s spiking input-output relation (Soudry & Meir 2014)", - "repository_type": "github", - "summary": "We derived in paper a method to find semi-analytic input-output relations for general HH-like neuron models (firing rates, spectra, linear filters)under sparse spike stimulation. Here we demonstrate the applicability of this method to various HH-type models (HH with slow sodium inactivation, with slow pottasium inactivation, with synaptic STD and other various extensions).", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1668, - "tag": "ModelDB:144993" - } - ], - "timestamp_created": "2024-01-12 09:39:47.611114+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/144993", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1148": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1148, - "name": "Hypocretin and Locus Coeruleus model neurons (Carter et al 2012)", - "repository_type": "github", - "summary": "Conductance based model of the hypocretin neurons (HCRT) and another one of the Locus Coeruleus one (LC). The HCRT drive the LCs via the HCRT receptor on the LCs. The LCs lead to the awakening of the mice if the number of spikes raises over 10 spikes in 10 seconds window.", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1669, - "tag": "ModelDB:145162" - } - ], - "timestamp_created": "2024-01-12 09:39:48.180474+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/145162", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1149": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1149, - "name": "Neurophysiological impact of inactivation pathways in A-type K+ channels (Fineberg et al 2012)", - "repository_type": "github", - "summary": "These models predict the differential effects of varying pathways of inactivation (closed state inactivation, CSI, or open state inactivation, OSI). Specifically, Markov models of Kv4 potassium channels with CSI or CSI+OSI were inserted into the CA1 pyramidal neuron model from Migliore et al (1999; ModelDB accession #2796) to determine the neurophysiological impact of inactivation pathways. Furthermore, Markov models of Kv4.2 and Kv3.4 channels are used to illustrate a method by which to test what pathway of inactivation a channel uses.", - "tags": [ - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 1670, - "tag": "IonChannelLab" - }, - { - "id": 748, - "tag": "Markov-type model" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1671, - "tag": "ModelDB:145672" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:39:48.686222+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/145672", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1150": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1150, - "name": "Circadian clock model based on protein sequestration (simple version) (Kim & Forger 2012)", - "repository_type": "github", - "summary": "\"\u2026 To understand the biochemical mechanisms of this timekeeping, we have developed a detailed mathematical model of the mammalian circadian clock. \r\n\r\nOur model can accurately predict diverse experimental data including the phenotypes of mutations or knockdown of clock genes as well as the time courses and relative expression of clock transcripts and proteins. \r\n\r\nUsing this model, we show how a universal motif of circadian timekeeping, where repressors tightly bind activators rather than directly binding to DNA, can generate oscillations when activators and repressors are in stoichiometric balance. \r\n\u2026\"", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 1586, - "tag": "Circadian Rhythms" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 1609, - "tag": "Mathematica" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1672, - "tag": "ModelDB:145800" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 704, - "tag": "SBML" - }, - { - "id": 569, - "tag": "Simplified Models" - }, - { - "id": 759, - "tag": "XPPAUT" - } - ], - "timestamp_created": "2024-01-12 09:39:49.301782+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/145800", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1151": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1151, - "name": "Circadian clock model in mammals (detailed version) (Kim & Forger 2012)", - "repository_type": "github", - "summary": "\"\u2026 To understand the biochemical mechanisms of this timekeeping, we have developed a detailed mathematical model of the mammalian circadian clock. \r\n\r\nOur model can accurately predict diverse experimental data including the phenotypes of mutations or knockdown of clock genes as well as the time courses and relative expression of clock transcripts and proteins. \r\n\r\nUsing this model, we show how a universal motif of circadian timekeeping, where repressors tightly bind activators rather than directly binding to DNA, can generate oscillations when activators and repressors are in stoichiometric balance. \r\n\u2026\"", - "tags": [ - { - "id": 1527, - "tag": "Brain Rhythms" - }, - { - "id": 1586, - "tag": "Circadian Rhythms" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 1609, - "tag": "Mathematica" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1673, - "tag": "ModelDB:145801" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 759, - "tag": "XPPAUT" - } - ], - "timestamp_created": "2024-01-12 09:39:49.797670+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/145801", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1152": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1152, - "name": "Oversampling method to extract excitatory and inhibitory conductances (Bedard et al. 2012)", - "repository_type": "github", - "summary": "\" ...\r\nWe present here a new method that allows\r\nextracting estimates of the full time course of excitatory and inhibitory conductances from single-trial\r\nVm recordings.\r\n\r\nThis method is based on oversampling of the Vm . We test the method numerically using\r\nmodels of increasing complexity.\r\n\r\nFinally, the method is evaluated using controlled conductance injection\r\nin cortical neurons in vitro using the dynamic-clamp technique.\r\n...\"", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1674, - "tag": "ModelDB:145803" - }, - { - "id": 568, - "tag": "Parameter Fitting" - } - ], - "timestamp_created": "2024-01-12 09:39:50.310006+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/145803", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1153": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1153, - "name": "A fast model of voltage-dependent NMDA Receptors (Moradi et al. 2013)", - "repository_type": "github", - "summary": "These are two or triple-exponential models of the voltage-dependent NMDA receptors. Conductance of these receptors increase voltage-dependently with a \"Hodgkin and Huxley-type\" gating style that is also depending on glutamate-binding. Time course of the gating of these receptors in response to glutamate are also changing voltage-dependently. Temperature sensitivity and desensitization of these receptor are also taken into account.\r\nThree previous kinetic models that are able to simulate the voltage-dependence of the NMDARs are also imported to the NMODL. These models are not temperature sensitive. \r\n\r\nThese models are compatible with the \"event delivery system\" of NEURON. Parameters that are reported in our paper are applicable to CA1 pyramidal cell dendrites.", - "tags": [ - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1675, - "tag": "ModelDB:145836" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 569, - "tag": "Simplified Models" - } - ], - "timestamp_created": "2024-01-12 09:39:51.240755+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/145836", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1154": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 1154, - "name": "A CORF computational model of a simple cell that relies on LGN input (Azzopardi & Petkov 2012)", - "repository_type": "github", - "summary": "\"...\r\n\r\nWe propose a computational model that uses as afferent inputs the responses of model LGN cells with center-surround receptive fields (RFs) and we refer to it as a Combination of Receptive Fields (CORF) model. \r\n\r\nWe use shifted gratings as test stimuli and simulated reverse correlation to explore the nature of the proposed model. \r\n\r\nWe study its behavior regarding the effect of contrast on its response and orientation bandwidth as well as the effect of an orthogonal mask on the response to an optimally oriented stimulus.\r\n\r\nWe also evaluate and compare the performances of the CORF and GF (Gabor Filter) models regarding contour detection, using two public data sets of images of natural scenes with associated contour ground truths.\r\n\r\n...\r\n\r\nThe proposed CORF model is more realistic than the GF model and is more effective in contour detection, which is assumed to be the primary biological role of simple cells.\"", - "tags": [ - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1676, - "tag": "ModelDB:145882" - }, - { - "id": 569, - "tag": "Simplified Models" - } - ], - "timestamp_created": "2024-01-12 09:39:51.867258+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/145882", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1155": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1155, - "name": "NMDA subunit effects on Calcium and STDP (Evans et al. 2012)", - "repository_type": "github", - "summary": "Effect of NMDA subunit on spike timing dependent plasticity.", - "tags": [ - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1677, - "tag": "ModelDB:145917" - }, - { - "id": 802, - "tag": "STDP" - } - ], - "timestamp_created": "2024-01-12 09:39:52.410637+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/145917", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1156": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1156, - "name": "A generic MAPK cascade model for random parameter sampling analysis (Mai and Liu 2013)", - "repository_type": "github", - "summary": "A generic three-tier MAPK cascade model constructed by comparing previous MAPK models covering a range of biosystems. Pseudo parameters and random sampling were employed for qualitative analysis. A range of kinetic behaviors of MAPK activation, including ultrasensitivity, bistability, transient activation and oscillation, were successfully reproduced in this generic model. The mechanisms were revealed by statistic analysis of the parameter sets.", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1678, - "tag": "ModelDB:146024" - } - ], - "timestamp_created": "2024-01-12 09:39:52.941607+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/146024", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1157": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1157, - "name": "A set of reduced models of layer 5 pyramidal neurons (Bahl et al. 2012)", - "repository_type": "github", - "summary": "These are the NEURON files for 10 different models of a reduced L5 pyramidal neuron. The parameters were obtained by automatically fitting the models to experimental data using a multi objective evolutionary search strategy. Details on the algorithm can be found at \r\nhttp://www.g-node.org/emoo and in Bahl et al. (2012).\r\n", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1679, - "tag": "ModelDB:146026" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 569, - "tag": "Simplified Models" - } - ], - "timestamp_created": "2024-01-12 09:39:53.550742+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/146026", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1158": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1158, - "name": "Olfactory bulb mitral cell gap junction NN model: burst firing and synchrony (O`Connor et al. 2012)", - "repository_type": "github", - "summary": "In a network of 6 mitral cells connected by gap junction in the apical dendrite tuft, continuous current injections of 0.06 nA are injected into 20 locations in the apical tufts of two of the mitral cells. The current injections into one of the cells starts 10 ms after the other to generate asynchronous firing in the cells (Migliore et al. 2005 protocol). Firing of the cells is asynchronous for the first 120 ms. However after the burst firing phase is completed the firing in all cells becomes synchronous.", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1680, - "tag": "ModelDB:146030" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 586, - "tag": "Synchronization" - } - ], - "timestamp_created": "2024-01-12 09:39:54.101276+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/146030", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1159": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1159, - "name": "Opponent-channel model of the cortical representation of auditory space (Briley et al., 2012)", - "repository_type": "github", - "summary": "This is the computational opponent-channel model used by Briley et al. (2012) to model electroencephalographic (EEG) responses from human auditory cortex to abrupt shifts in sound-source location and to predict psychophysical measures of spatial acuity. The zip file contains both a Matlab and an Excel implementation of the model. Details of use are contained within each file.", - "tags": [ - { - "id": 762, - "tag": "Audition" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1681, - "tag": "ModelDB:146050" - }, - { - "id": 758, - "tag": "Sensory processing" - } - ], - "timestamp_created": "2024-01-12 09:39:54.624411+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/146050", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1160": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1160, - "name": "Ca1 pyramidal neuron: reduction model (Marasco et al. 2012)", - "repository_type": "github", - "summary": "\"... Here we introduce a new, automatic and fast method to map realistic neurons into equivalent reduced models running up to >40 times faster while maintaining a very high accuracy of the membrane potential dynamics during synaptic inputs, and a direct link with experimental observables. The mapping of arbitrary sets of synaptic inputs, without additional fine tuning, would also allow the convenient and efficient implementation of a new generation of large-scale simulations of brain regions reproducing the biological variability observed in real neurons, with unprecedented advances to understand higher brain functions.\"", - "tags": [ - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1682, - "tag": "ModelDB:146376" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 569, - "tag": "Simplified Models" - } - ], - "timestamp_created": "2024-01-12 09:39:55.139961+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/146376", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1161": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1161, - "name": "Mean Field Equations for Two-Dimensional Integrate and Fire Models (Nicola and Campbell, 2013)", - "repository_type": "github", - "summary": "The zip file contains the files used to perform numerical simulation and bifurcation studies of large networks of two-dimensional integrate and fire neurons and of the corresponding mean field models derived in our paper. The neural models used are the Izhikevich model and the Adaptive Exponential model.", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1683, - "tag": "ModelDB:146499" - } - ], - "timestamp_created": "2024-01-12 09:39:55.667146+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/146499", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1162": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1162, - "name": "Paradoxical GABA-mediated excitation (Lewin et al. 2012)", - "repository_type": "github", - "summary": "\"GABA is the key inhibitory neurotransmitter in the adult central nervous system, but in some circumstances can lead to a paradoxical excitation that has been causally implicated in diverse pathologies from endocrine stress responses to diseases of excitability including neuropathic pain and temporal lobe epilepsy. \r\n\r\nWe undertook a computational modeling approach to determine plausible ionic mechanisms of GABAA-dependent excitation in isolated post-synaptic CA1 hippocampal neurons because it may constitute a trigger for pathological synchronous epileptiform discharge. \r\n\r\nIn particular, the interplay intracellular chloride accumulation via the GABAA receptor and extracellular potassium accumulation via the K/Cl co-transporter KCC2 in promoting GABAA-mediated excitation is complex. \r\n...\"", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 1684, - "tag": "Ca pump" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 1685, - "tag": "KCC2" - }, - { - "id": 748, - "tag": "Markov-type model" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1686, - "tag": "ModelDB:146504" - }, - { - "id": 1687, - "tag": "NKCC1" - }, - { - "id": 738, - "tag": "Na/Ca exchanger" - }, - { - "id": 740, - "tag": "Na/K pump" - } - ], - "timestamp_created": "2024-01-12 09:39:56.224379+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/146504", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1163": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1163, - "name": "Functional impact of dendritic branch point morphology (Ferrante et al., 2013)", - "repository_type": "github", - "summary": "\" ... Here, we first quantified the morphological variability of branch points from two-photon images of rat CA1 pyramidal neurons. We then investigated the geometrical features affecting spike initiation, propagation, and timing with a computational model validated by glutamate uncaging experiments. The results suggest that even subtle membrane readjustments at branch point could drastically alter the ability of synaptic input to generate, propagate, and time action potentials.\"", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 804, - "tag": "Bifurcation" - }, - { - "id": 745, - "tag": "Conduction failure" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 1478, - "tag": "Information transfer" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1688, - "tag": "ModelDB:146509" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:39:56.900594+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/146509", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1164": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1164, - "name": "Reconstrucing sleep dynamics with data assimilation (Sedigh-Sarvestani et al., 2012)", - "repository_type": "github", - "summary": "We have developed a framework, based on the unscented Kalman filter, for estimating hidden states and parameters of a network model of sleep. The network model includes firing rates and neurotransmitter output of 5 cell-groups in the rat brain.", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1689, - "tag": "ModelDB:146554" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 592, - "tag": "Sleep" - }, - { - "id": 732, - "tag": "Tutorial/Teaching" - }, - { - "id": 1690, - "tag": "unscented Kalman filter" - } - ], - "timestamp_created": "2024-01-12 09:39:57.497382+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/146554", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1165": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1165, - "name": "Nonlinear dendritic processing in barrel cortex spiny stellate neurons (Lavzin et al. 2012)", - "repository_type": "github", - "summary": "This is a multi-compartmental simulation of a spiny stellate neuron which is stimulated by a thalamocortical (TC) and cortico-cortical (CC) inputs. No other cells are explicitly modeled; the presynaptic network activation is represented by the number of active synapses. Preferred and non \u2013preferred thalamic directions thus correspond to larder/smaller number of TC synapses. This simulation revealed that randomly activated synapses can cooperatively trigger global NMDA spikes, which involve participation of most of the dendritic tree. Surprisingly, we found that although the voltage profile of the cell was uniform, the calcium influx was restricted to \u2018hot spots\u2019 which correspond to synaptic clusters or large conductance synapses", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 1684, - "tag": "Ca pump" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 860, - "tag": "Direction Selectivity" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1691, - "tag": "ModelDB:146565" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 1507, - "tag": "Whisking" - } - ], - "timestamp_created": "2024-01-12 09:39:58.143979+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/146565", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1166": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1166, - "name": "Olfactory bulb network: neurogenetic restructuring and odor decorrelation (Chow et al. 2012)", - "repository_type": "github", - "summary": "Adult neurogenesis in the olfactory bulb has been shown experimentally\r\nto contribute to perceptual learning. Using a computational network\r\nmodel we show that fundamental aspects of the adult neurogenesis\r\nobserved in the olfactory bulb -- the persistent addition of new\r\ninhibitory granule cells to the network, their activity-dependent\r\nsurvival, and the reciprocal character of their synapses with the\r\nprincipal mitral cells -- are sufficient to restructure the network\r\nand to alter its encoding of odor stimuli adaptively so as to reduce\r\nthe correlations between the bulbar representations of similar\r\nstimuli. The model captures the experimentally observed\r\nrole of neurogenesis in perceptual learning and the enhanced response\r\nof young granule cells to novel stimuli. Moreover, it makes specific\r\npredictions for the type of odor enrichment that should be effective\r\nin enhancing the ability of animals to discriminate similar odor\r\nmixtures. NSF grant DMS-0719944.\r\n", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 1662, - "tag": "Apoptosis" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1692, - "tag": "ModelDB:146583" - }, - { - "id": 1693, - "tag": "Neurogenesis" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 791, - "tag": "Rate-coding model neurons" - }, - { - "id": 758, - "tag": "Sensory processing" - } - ], - "timestamp_created": "2024-01-12 09:39:58.652137+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/146583", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1167": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1167, - "name": "Modeling the effects of dopamine on network synchronization (Komek et al. 2012)", - "repository_type": "github", - "summary": "Dopamine modulates cortical circuit activity in part through its actions on GABAergic interneurons, including increasing the excitability of fast-spiking interneurons. Though such effects have been demonstrated in single cells, there are no studies that examine how such mechanisms may lead to the effects of dopamine at a neural network level. In this study, we investigated the effects of dopamine on synchronization in two simulated neural networks; one biophysical model composed of Wang-Buzsaki neurons and a reduced model with theta neurons. In both models, we show that parametrically varying the levels of dopamine, modeled through the changes in the excitability of interneurons, reveals an inverted-U shaped relationship, with low gamma band power at both low and high dopamine levels and optimal synchronization at intermediate levels. Moreover, such a relationship holds when the external input is both tonic and periodic at gamma band range. Together, our results indicate that dopamine can modulate cortical gamma band synchrony in an inverted-U fashion and that the physiologic effects of dopamine on single fast-spiking interneurons can give rise to such non-monotonic effects at the network level.", - "tags": [ - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1694, - "tag": "ModelDB:146734" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 759, - "tag": "XPPAUT" - } - ], - "timestamp_created": "2024-01-12 09:39:59.331023+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/146734", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1168": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1168, - "name": "ACh modulation in olfactory bulb and piriform cortex (de Almeida et al. 2013;Devore S, et al. 2014)", - "repository_type": "github", - "summary": "This matlab code was used in the papers de Almeida, Idiart and Linster, (2013), Devore S, de Almeida L, Linster C (2014) .\r\nThis work uses a computational model of the OB and PC and their common cholinergic inputs to investigate how bulbar cholinergic modulation affects cortical odor processing.", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1695, - "tag": "ModelDB:146813" - }, - { - "id": 874, - "tag": "Noise Sensitivity" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 586, - "tag": "Synchronization" - } - ], - "timestamp_created": "2024-01-12 09:40:00.128228+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/146813", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1169": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1169, - "name": "Motor cortex microcircuit simulation based on brain activity mapping (Chadderdon et al. 2014)", - "repository_type": "github", - "summary": "\"...\r\nWe developed a computational\r\nmodel based primarily on a unified set of brain activity mapping\r\nstudies of mouse M1.\r\nThe simulation consisted of 775 spiking neurons of\r\n10 cell types with detailed population-to-population connectivity.\r\n\r\nStatic\r\nanalysis of connectivity with graph-theoretic tools revealed that the corticostriatal\r\npopulation showed strong centrality, suggesting that would\r\nprovide a network hub.\r\n...\r\nBy demonstrating the effectiveness of combined static\r\nand dynamic analysis, our results show how static brain maps can be\r\nrelated to the results of brain activity mapping.\"", - "tags": [ - { - "id": 856, - "tag": "Laminar Connectivity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1696, - "tag": "ModelDB:146949" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - } - ], - "timestamp_created": "2024-01-12 09:40:00.760287+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/146949", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1170": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1170, - "name": "Constructed Tessellated Neuronal Geometries (CTNG) (McDougal et al. 2013)", - "repository_type": "github", - "summary": "We present an algorithm to form watertight 3D surfaces consistent with\r\nthe point-and-diameter based neuronal morphology descriptions widely\r\nused with spatial electrophysiology simulators.\r\n\r\n...\r\n\r\nThis (point-and-diameter)\r\nrepresentation is well-suited for electrophysiology simulations, where\r\nthe space constants are larger than geometric ambiguities. However,\r\nthe simple interpretations used for pure electrophysiological\r\nsimulation produce geometries unsuitable for multi-scale models that\r\nalso involve three-dimensional reaction\u2013diffusion, as such models have\r\nsmaller space constants.\r\n\r\n...\r\n\r\nAlthough one cannot exactly reproduce an\r\noriginal neuron's full shape from point-and-diameter data, our new\r\nconstructive tessellated neuronal geometry (CTNG) algorithm uses\r\nconstructive solid geometry to define a plausible reconstruction\r\nwithout gaps or cul-de-sacs.\r\n\r\nCTNG then uses \u201cconstructive cubes\u201d to\r\nproduce a watertight triangular mesh of the neuron surface, suitable\r\nfor use in reaction\u2013diffusion simulations.\r\n...\"", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 1697, - "tag": "Cython" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1698, - "tag": "ModelDB:146950" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 620, - "tag": "Python" - } - ], - "timestamp_created": "2024-01-12 09:40:01.259052+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/146950", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1171": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1171, - "name": "Motion Clouds: Synthesis of random textures for motion perception (Leon et al. 2012)", - "repository_type": "github", - "summary": "We describe a framework to generate random texture movies with controlled information content. In particular, these stimuli can be made closer to naturalistic textures compared to usual stimuli such as gratings and random-dot kinetograms. We simplified the definition to parametrically define these \"Motion Clouds\" around the most prevalent feature axis (mean and bandwith): direction, spatial frequency, orientation.", - "tags": [ - { - "id": 1466, - "tag": "Envelope synthesis" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1699, - "tag": "ModelDB:146953" - }, - { - "id": 1700, - "tag": "Motion Detection" - }, - { - "id": 874, - "tag": "Noise Sensitivity" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 719, - "tag": "Pattern Recognition" - }, - { - "id": 850, - "tag": "Perceptual Categories" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 758, - "tag": "Sensory processing" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - } - ], - "timestamp_created": "2024-01-12 09:40:01.810345+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/146953", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1172": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1172, - "name": "A large-scale model of the functioning brain (spaun) (Eliasmith et al. 2012)", - "repository_type": "github", - "summary": "\" ... In this work, we present a\r\n2.5-million-neuron model of the brain (called \u201cSpaun\u201d) that bridges this gap (between neural activity and biological function) by exhibiting many different\r\nbehaviors. The model is presented only with visual image sequences, and it draws all of its responses with\r\na physically modeled arm. Although simplified, the model captures many aspects of neuroanatomy,\r\nneurophysiology, and psychological behavior, which we demonstrate via eight diverse tasks.\"", - "tags": [ - { - "id": 825, - "tag": "Learning" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1701, - "tag": "ModelDB:147103" - }, - { - "id": 678, - "tag": "Nengo" - }, - { - "id": 809, - "tag": "Reinforcement Learning" - }, - { - "id": 726, - "tag": "Vision" - } - ], - "timestamp_created": "2024-01-12 09:40:02.343828+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/147103", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1173": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1173, - "name": "Structure-dynamics relationships in bursting neuronal networks revealed (M\u00e4ki-Marttunen et al. 2013)", - "repository_type": "github", - "summary": "This entry includes tools for generating and analyzing network structure, and for running the neuronal network simulations on them.", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 855, - "tag": "Connectivity matrix" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1702, - "tag": "ModelDB:147117" - }, - { - "id": 611, - "tag": "NEST" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - }, - { - "id": 586, - "tag": "Synchronization" - } - ], - "timestamp_created": "2024-01-12 09:40:02.869356+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/147117", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1174": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1174, - "name": "Synaptic scaling balances learning in a spiking model of neocortex (Rowan & Neymotin 2013)", - "repository_type": "github", - "summary": "Learning in the brain requires complementary mechanisms: potentiation and activity-dependent homeostatic scaling. We introduce synaptic scaling to a biologically-realistic spiking model of neocortex which can learn changes in oscillatory rhythms using STDP, and show that scaling is necessary to balance both positive and negative changes in input from potentiation and atrophy. We discuss some of the issues that arise when considering synaptic scaling in such a model, and show that scaling regulates activity whilst allowing learning to remain unaltered.", - "tags": [ - { - "id": 75, - "tag": "Homeostasis" - }, - { - "id": 825, - "tag": "Learning" - }, - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1703, - "tag": "ModelDB:147141" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 802, - "tag": "STDP" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - } - ], - "timestamp_created": "2024-01-12 09:40:03.439537+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/147141", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1175": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1175, - "name": "Biophysical model for field potentials of networks of I&F neurons (beim Graben & Serafim 2013)", - "repository_type": "github", - "summary": "\"...\r\nStarting from a reduced three-compartment model of a single pyramidal neuron, we derive an observation model for dendritic dipole currents in extracellular space and thereby for the dendritic field potential (DFP) that contributes to the local field potential (LFP) of a neural population. \r\n...\r\nOur reduced three-compartment scheme allows to derive networks of leaky integrate-and-fire (LIF) models, which facilitates comparison with existing neural network and observation models. \r\n...\"", - "tags": [ - { - "id": 645, - "tag": "Brian" - }, - { - "id": 734, - "tag": "Extracellular Fields" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1704, - "tag": "ModelDB:147172" - }, - { - "id": 620, - "tag": "Python" - } - ], - "timestamp_created": "2024-01-12 09:40:03.972345+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/147172", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1176": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 1176, - "name": "Neuronvisio: a gui with 3D capabilities for NEURON (Mattioni et al. 2012)", - "repository_type": "github", - "summary": "\"The NEURON simulation environment is a commonly used tool to perform electrical simulation of neurons and neuronal networks. \r\n\r\nThe NEURON User Interface, based on the now discontinued InterViews library, provides some limited facilities to explore models and to plot their simulation results. \r\n\r\nOther limitations include the inability to generate a three-dimensional visualization, no standard mean to save the results of simulations, or to store the model geometry within the results. \r\n\r\nNeuronvisio (http://neuronvisio.org) aims to address these deficiencies through a set of well designed python APIs and provides an improved UI, allowing users to explore and interact with the model. \r\n...\"", - "tags": [ - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1705, - "tag": "ModelDB:147185" - }, - { - "id": 848, - "tag": "NeuroML (web link to model)" - }, - { - "id": 1706, - "tag": "Neuronvisio (web link to model)" - } - ], - "timestamp_created": "2024-01-12 09:40:04.481207+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/147185", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1177": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1177, - "name": "Dendritic signals command firing dynamics in a Cerebellar Purkinje Cell model (Genet et al. 2010)", - "repository_type": "github", - "summary": "This model endows the dendrites of a reconstructed Purkinje cells (PC) with the mechanism of Ca-dependent plateau potentials and spikes described in Genet, S., and B. Delord. 2002. A biophysical model of nonlinear dynamics underlying plateau potentials and calcium spikes in Purkinje cell dendrites. J. Neurophysiol. 88:2430\u20132444). It is a part of a comprehensive mathematical study suggesting that active electric signals in the dendrites of PC command epochs of firing and silencing of the PC soma.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 1684, - "tag": "Ca pump" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 1606, - "tag": "Conductance distributions" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 1560, - "tag": "I Ca,p" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1707, - "tag": "ModelDB:147218" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 569, - "tag": "Simplified Models" - } - ], - "timestamp_created": "2024-01-12 09:40:05.022354+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/147218", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1178": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1178, - "name": "Composite spiking network/neural field model of Parkinsons (Kerr et al 2013)", - "repository_type": "github", - "summary": "This code implements a composite model of Parkinson's disease (PD). The\r\ncomposite model consists of a leaky integrate-and-fire spiking neuronal\r\nnetwork model being driven by output from a neural field model (instead\r\nof the more usual white noise drive). Three different sets of parameters\r\nwere used for the field model: one with basal ganglia parameters based\r\non data from healthy individuals, one based on data from individuals\r\nwith PD, and one purely thalamocortical model. The aim of this model is\r\nto explore how the different dynamical patterns in each each of these\r\nfield models affects the activity in the network model.", - "tags": [ - { - "id": 765, - "tag": "I Chloride" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 1478, - "tag": "Information transfer" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1708, - "tag": "ModelDB:147366" - }, - { - "id": 743, - "tag": "NEURON (web link to model)" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 620, - "tag": "Python" - } - ], - "timestamp_created": "2024-01-12 09:40:05.582446+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/147366", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1179": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1179, - "name": "IP3R model comparison (Hituri and Linne 2013)", - "repository_type": "github", - "summary": "In this study, four models of IP3R (Othmer and Tang, 1993; Dawson et al., 2003; Fraiman and Dawson, 2004; Doi et al., 2005) were selected among many to examine their behavior and compare them with experimental data available in literature.\r\n\r\nThe provided MATLAB script (run_IP3R_P0.m) will run the simulations and plot Figure 2A in \r\nthe paper.\r\n", - "tags": [ - { - "id": 790, - "tag": "Calcium waves" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1709, - "tag": "ModelDB:147367" - }, - { - "id": 1574, - "tag": "STEPS" - } - ], - "timestamp_created": "2024-01-12 09:40:06.157354+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/147367", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1180": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1180, - "name": "Direct recruitment of S1 pyramidal cells and interneurons via ICMS (Overstreet et al., 2013)", - "repository_type": "github", - "summary": "Study of the pyramidal cells and interneurons recruited by intracortical microstimulation in primary somatosensory cortex. Code includes morphological models for seven types of pyramidal cells and eight types of interneurons, NEURON code to simulate ICMS, and an artificial reconstruction of a 3D slab of cortex implemented in MATLAB.", - "tags": [ - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 783, - "tag": "I_Ks" - }, - { - "id": 1710, - "tag": "Intracortical Microstimulation" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1711, - "tag": "ModelDB:147460" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:40:06.681448+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/147460", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1181": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1181, - "name": "Storing serial order in intrinsic excitability: a working memory model (Conde-Sousa & Aguiar 2013)", - "repository_type": "github", - "summary": "\" \u2026 Here we present a model for working\r\nmemory which relies on the modulation of the intrinsic excitability properties of neurons, instead of synaptic plasticity, to retain novel information for periods of seconds to minutes. \r\n\r\nWe show that it is possible to effectively use this mechanism to store the serial order in a sequence of patterns of activity.\r\n\u2026\r\nThe presented model exhibits properties which\r\nare in close agreement with experimental results in working\r\nmemory. ...\r\n\"\r\n", - "tags": [ - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1712, - "tag": "ModelDB:147461" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 794, - "tag": "Working memory" - } - ], - "timestamp_created": "2024-01-12 09:40:07.182857+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/147461", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1182": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 1182, - "name": "A detailed and fast model of extracellular recordings (Camunas-Mesa & Qurioga 2013)", - "repository_type": "github", - "summary": "\"We present a novel method to generate realistic simulations of extracellular recordings. The simulations were obtained by superimposing the activity of neurons placed randomly in a cube of brain tissue. Detailed models of individual neurons were used to reproduce the extracellular action potentials of close-by neurons. ...\"", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 734, - "tag": "Extracellular Fields" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1713, - "tag": "ModelDB:147487" - } - ], - "timestamp_created": "2024-01-12 09:40:07.707957+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/147487", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1183": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1183, - "name": "Altered complexity in layer 2/3 pyramidal neurons (Luuk van der Velden et al. 2012)", - "repository_type": "github", - "summary": "\" ... Our experimental results show that hypercomplexity of the apical dendritic tuft of layer 2/3 pyramidal neurons affects neuronal excitability by reducing the amount of spike frequency adaptation. \r\n\r\nThis difference in firing pattern, related to a higher dendritic complexity, was accompanied by an altered development of the afterhyperpolarization slope with successive action potentials. \r\n\r\nOur abstract and realistic neuronal models, which allowed manipulation of the dendritic complexity, showed similar effects on neuronal excitability and confirmed the impact of apical dendritic complexity.\r\n\r\nAlterations of dendritic complexity, as observed in several pathological conditions such as neurodegenerative diseases or neurodevelopmental disorders, may thus not only affect the input to layer 2/3 pyramidal neurons but also shape their firing pattern and consequently alter the information processing in the cortex.\"", - "tags": [ - { - "id": 1684, - "tag": "Ca pump" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1714, - "tag": "ModelDB:147514" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:40:08.205586+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/147514", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1184": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1184, - "name": "BCM-like synaptic plasticity with conductance-based models (Narayanan Johnston, 2010)", - "repository_type": "github", - "summary": "\" ...\r\nAlthough the BCM-like plasticity framework\r\nhas been a useful formulation to understand synaptic plasticity\r\nand metaplasticity, a mechanism for the activity-dependent regulation\r\nof this modification threshold has remained an open question. In this\r\nsimulation study based on CA1 pyramidal cells, we use a modification\r\nof the calcium-dependent hypothesis proposed elsewhere and show\r\nthat a change in the hyperpolarization-activated, nonspecific-cation h\r\ncurrent is capable of shifting the modification threshold. \r\n...\"", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1715, - "tag": "ModelDB:147538" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - } - ], - "timestamp_created": "2024-01-12 09:40:08.776645+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/147538", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1185": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1185, - "name": "Resonance properties through Chirp stimulus responses (Narayanan Johnston 2007, 2008)", - "repository_type": "github", - "summary": "...we constructed a simple, single-compartment\r\nmodel with Ih as the only active current... we found that both resonance frequency and resonance strength increased monotonically with the increase in the h conductance, supporting the notion of a direct, graded relationship between h conductance and resonance properties... (Narayanan and Johnston, 2007). ...we show that the h channels introduce an apparent negative delay in the local voltage response of these neurons with respect to the injected current within the theta frequency range... we found that the total inductive phase increased monotonically with the h conductance, whereas it had a bell-shaped dependence on both the membrane voltage and the half-maximal activation voltage for the h conductance. (Narayanan and Johnston, 2008).", - "tags": [ - { - "id": 1606, - "tag": "Conductance distributions" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1716, - "tag": "ModelDB:147539" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:40:09.336859+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/147539", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1186": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1186, - "name": "Chirp stimulus responses in a morphologically realistic model (Narayanan and Johnston, 2007)", - "repository_type": "github", - "summary": "...we built a multicompartmental model with a morphologically realistic three-dimensional reconstruction of a CA1 pyramidal neuron. The only active conductance we added to the model was the h conductance. ... We conclude that experimentally observed gradient in density of h channels could theoretically account for experimentally observed gradient in resonance properties (Narayanan and Johnston, 2007).", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1717, - "tag": "ModelDB:147578" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:40:09.871213+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/147578", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1187": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 1187, - "name": "Cytoplasmic electric fields and electroosmosis (Andreev 2013)", - "repository_type": "github", - "summary": "The paper presents two mathematical models describing the role of electroosmosis in the transport of the negatively charged messenger proteins to the negatively charged nucleus and in the recovery of the fluorescence after photobleaching. The parameters of the models were derived from the extensive review of the literature data. Computer simulations were performed within the COMSOL 4.2a software environment. The first model demonstrated that the presence of electroosmosis might intensify the flux of messenger proteins to the nucleus and allow the efficient transport of the negatively charged phosphorylated messenger proteins against the electrostatic repulsion of the negatively charged nucleus. The second model revealed that the presence of the electroosmotic flow made the time of fluorescence recovery dependent on the position of the bleaching spot relative to cellular membrane.", - "tags": [ - { - "id": 1718, - "tag": "COMSOL (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1719, - "tag": "ModelDB:147740" - } - ], - "timestamp_created": "2024-01-12 09:40:10.370564+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/147740", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1188": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1188, - "name": "Human sleep/wake cycle (Rempe et al. 2010)", - "repository_type": "github", - "summary": "This model simulates sleep in the human brain and is consistent with both the flip/flop concept and the two-process model of sleep regulation. The model also gives a possible mechanism for the changes in sleep timing seen in narcolepsy.", - "tags": [ - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1720, - "tag": "ModelDB:147748" - }, - { - "id": 592, - "tag": "Sleep" - }, - { - "id": 759, - "tag": "XPPAUT" - } - ], - "timestamp_created": "2024-01-12 09:40:10.858170+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/147748", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1189": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1189, - "name": "Region-specific atrophy in dendrites (Narayanan, Narayan, Chattarji, 2005)", - "repository_type": "github", - "summary": "...in this study, we develop an algorithm that uses statistics from precise morphometric analyses to systematically remodel neuronal reconstructions. We use the distribution function of the ratio of two normal distributed random variables to specify the probabilities of remodeling along various regions of the dendritic arborization. We then use these probabilities to drive an iterative algorithm for manipulating the dendritic tree in a region-specific manner. As a test, we apply this framework to a well characterized example of dendritic remodeling: stress-induced dendritic atrophy in hippocampal CA3 pyramidal cells. We show that our pruning algorithm is capable of eliciting atrophy that matches biological data from rodent models of chronic stress.\r\n
", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1721, - "tag": "ModelDB:147756" - } - ], - "timestamp_created": "2024-01-12 09:40:11.352433+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/147756", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1190": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1190, - "name": "Amyloid-beta effects on release probability and integration at CA3-CA1 synapses (Romani et al. 2013)", - "repository_type": "github", - "summary": "The role of amyloid beta (A\u00df) in brain function and in the pathogenesis of Alzheimer\u2019s disease remains elusive.\r\nRecent publications reported that an increase in A\u00df concentration perturbs presynaptic release in hippocampal neurons, in particular by increasing release probability of CA3-CA1 synapses. The model predics how this alteration can affect synaptic plasticity and signal integration. The results suggest that the perturbation of release probability induced by increased A\u00df can significantly alter the spike probability of CA1 pyramidal neurons and thus contribute to abnormal hippocampal function during Alzheimer\u2019s disease.", - "tags": [ - { - "id": 827, - "tag": "Aging/Alzheimer`s" - }, - { - "id": 722, - "tag": "Depression" - }, - { - "id": 723, - "tag": "Facilitation" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 1433, - "tag": "I_AHP" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1722, - "tag": "ModelDB:147757" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - } - ], - "timestamp_created": "2024-01-12 09:40:11.876408+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/147757", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1191": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1191, - "name": "Impact of dendritic atrophy on intrinsic and synaptic excitability (Narayanan & Chattarji, 2010)", - "repository_type": "github", - "summary": "These simulations examined the atrophy induced changes in electrophysiological properties of CA3 pyramidal neurons. We found these neurons change from bursting to regular spiking as atrophy increases. Region-specific atrophy induced region-specific increases in synaptic excitability in a passive dendritic tree. All dendritic compartments of an atrophied \r\nneuron had greater synaptic excitability and a larger voltage transfer to the soma than the control neuron.\r\n", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 1606, - "tag": "Conductance distributions" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 1433, - "tag": "I_AHP" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1723, - "tag": "ModelDB:147867" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:40:12.496722+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/147867", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1192": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1192, - "name": "Development of orientation-selective simple cell receptive fields (Rishikesh and Venkatesh, 2003)", - "repository_type": "github", - "summary": "Implementation of a computational model for the development of\r\nsimple-cell receptive fields spanning the regimes before and after eye-opening. The before eye-opening period is governed by a correlation-based rule from Miller (Miller, J. Neurosci., 1994), and the post eye-opening period is governed by a self-organizing, experience-dependent dynamics derived in the reference below.", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 793, - "tag": "Development" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1724, - "tag": "ModelDB:147929" - }, - { - "id": 1569, - "tag": "Orientation selectivity" - }, - { - "id": 791, - "tag": "Rate-coding model neurons" - }, - { - "id": 803, - "tag": "Unsupervised Learning" - }, - { - "id": 726, - "tag": "Vision" - }, - { - "id": 822, - "tag": "Winner-take-all" - } - ], - "timestamp_created": "2024-01-12 09:40:13.211196+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/147929", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1193": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 1193, - "name": "Towards a virtual C. elegans (Palyanov et al. 2012)", - "repository_type": "github", - "summary": "\"... Here we present a detailed demonstration of a virtual C. elegans\r\naimed at integrating these data in the form of a 3D dynamic model operating in a simulated physical environment. Our current\r\ndemonstration includes a realistic flexible worm body model, muscular system and a partially implemented ventral neural cord.\r\nOur virtual C. elegans demonstrates successful forward and backward locomotion when sending sinusoidal patterns of neuronal\r\nactivity to groups of motor neurons. \r\n...\"", - "tags": [ - { - "id": 776, - "tag": "C or Cplusplus program (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1725, - "tag": "ModelDB:147938" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - } - ], - "timestamp_created": "2024-01-12 09:40:13.726219+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/147938", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1194": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1194, - "name": "Spiking GridPlaceMap model (Pilly & Grossberg, PLoS One, 2013)", - "repository_type": "github", - "summary": "Development of spiking grid cells and place cells in the entorhinal-hippocampal system to represent positions in large spaces", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 1527, - "tag": "Brain Rhythms" - }, - { - "id": 855, - "tag": "Connectivity matrix" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 793, - "tag": "Development" - }, - { - "id": 1461, - "tag": "Grid cell" - }, - { - "id": 1531, - "tag": "I Cl, leak" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 825, - "tag": "Learning" - }, - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1726, - "tag": "ModelDB:148035" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 719, - "tag": "Pattern Recognition" - }, - { - "id": 799, - "tag": "Place cell/field" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 803, - "tag": "Unsupervised Learning" - } - ], - "timestamp_created": "2024-01-12 09:40:14.249238+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/148035", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1195": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1195, - "name": "CA1 pyramidal neuron: effects of R213Q and R312W Kv7.2 mutations (Miceli et al. 2013)", - "repository_type": "github", - "summary": "NEURON mod files from the paper:\r\n\r\nMiceli et al, Genotype\u2013phenotype correlations in neonatal epilepsies caused by mutations in the voltage sensor of Kv7.2 potassium channel subunits, PNAS 2013 Feb 25. [Epub ahead of print]\r\n\r\nIn this paper, functional studies revealed that in homomeric or heteromeric configuration with KV7.2 and/or KV7.3 subunits, R213W and \r\nR213Q mutations markedly destabilized the open state, causing a dramatic decrease in channel voltage sensitivity.\r\nModeling these channels in CA1 hippocampal pyramidal cells revealed that both mutations increased cell firing frequency,\r\nwith the R213Q mutation prompting more dramatic functional changes compared with the R213W mutation.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1727, - "tag": "ModelDB:148094" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:40:14.782545+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/148094", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1196": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1196, - "name": "Effects of Chloride accumulation and diffusion on GABAergic transmission (Jedlicka et al 2011)", - "repository_type": "github", - "summary": "\"In the CNS, prolonged activation of GABA(A) receptors (GABA(A)Rs) has been shown to evoke biphasic postsynaptic responses, consisting of an initial hyperpolarization followed by a depolarization. \r\n\r\nA potential mechanism underlying the depolarization is an acute chloride (Cl(-)) accumulation resulting in a shift of the GABA(A) reversal potential (E(GABA)).\r\n\r\nThe amount of GABA-evoked Cl(-) accumulation and accompanying depolarization depends on presynaptic and postsynaptic properties of GABAergic transmission, as well as on cellular morphology and regulation of Cl(-) intracellular concentration ([Cl(-)](i)).\r\n\r\nTo analyze the influence of these factors on the Cl(-) and voltage behavior, we studied spatiotemporal dynamics of activity-dependent [Cl(-)](i) changes in multicompartmental models of hippocampal cells based on realistic morphological data.\r\n...\"", - "tags": [ - { - "id": 1592, - "tag": "Chloride regulation" - }, - { - "id": 765, - "tag": "I Chloride" - }, - { - "id": 1728, - "tag": "I_HCO3" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1729, - "tag": "ModelDB:148253" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - } - ], - "timestamp_created": "2024-01-12 09:40:15.296907+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/148253", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1197": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1197, - "name": "Circadian clock model in mammals (PK/PD model) (Kim & Forger 2013)", - "repository_type": "github", - "summary": "A systems pharmacology model of the mammalian circadian clock including PF-670462 (CK1d/e inhibitor).", - "tags": [ - { - "id": 1586, - "tag": "Circadian Rhythms" - }, - { - "id": 1609, - "tag": "Mathematica" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1730, - "tag": "ModelDB:148320" - }, - { - "id": 823, - "tag": "Phase Response Curves" - }, - { - "id": 746, - "tag": "Therapeutics" - } - ], - "timestamp_created": "2024-01-12 09:40:15.851492+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/148320", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1198": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1198, - "name": "Failure of Deep Brain Stimulation in a basal ganglia neuronal network model (Dovzhenok et al. 2013)", - "repository_type": "github", - "summary": "\"\u2026 Recently, a lot of interest has been devoted to desynchronizing delayed feedback deep brain stimulation (DBS).\r\n...\r\nThis study explores the action of delayed feedback stimulation on partially synchronized oscillatory dynamics, similar to what one observes experimentally in parkinsonian patients.\r\n\u2026\"\r\n\r\nImplemented by Andrey Dovzhenok, to whom questions should be addressed.", - "tags": [ - { - "id": 831, - "tag": "Deep brain stimulation" - }, - { - "id": 1560, - "tag": "I Ca,p" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 1433, - "tag": "I_AHP" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1731, - "tag": "ModelDB:148637" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 759, - "tag": "XPPAUT" - } - ], - "timestamp_created": "2024-01-12 09:40:16.437572+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/148637", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1199": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1199, - "name": "Neuronal morphology goes digital ... (Parekh & Ascoli 2013)", - "repository_type": "github", - "summary": "An illustration of a NEURON model and why reconstructing morphologies\r\nis useful in this regard (i.e. investigating spatial/temporal aspect\r\nof how different currents and voltage propagate in dendrites).", - "tags": [ - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1732, - "tag": "ModelDB:148644" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 732, - "tag": "Tutorial/Teaching" - } - ], - "timestamp_created": "2024-01-12 09:40:16.951734+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/148644", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1200": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1200, - "name": "CA1 pyramidal neuron: action potential backpropagation (Gasparini & Migliore 2015)", - "repository_type": "github", - "summary": "\" ... the investigation of AP backpropagation and its functional roles has greatly benefitted from computational models that use biophysically and morphologically accurate implementations. ...\" This model entry recreates figures 2 and 4 from the paper illustrating how conductance densities of voltage gated channels (fig 2) and the timing of synaptic input with backpropagating action potentials (fig 4) affects membrane voltage trajectories.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1733, - "tag": "ModelDB:148646" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 596, - "tag": "Synaptic Integration" - } - ], - "timestamp_created": "2024-01-12 09:40:17.465580+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/148646", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1201": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1201, - "name": "Using Strahler's analysis to reduce realistic models (Marasco et al, 2013)", - "repository_type": "github", - "summary": "Building on our previous work (Marasco et al., (2012)), we present a general reduction method based on Strahler's analysis of neuron\r\nmorphologies. We show that, without any fitting or tuning procedures, it is\r\npossible to map any morphologically and biophysically accurate neuron model\r\ninto an equivalent reduced version. Using this method for Purkinje cells, we\r\ndemonstrate how run times can be reduced up to 200-fold, while accurately taking into account the effects of arbitrarily located and activated\r\nsynaptic inputs.\r\n", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 1684, - "tag": "Ca pump" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1734, - "tag": "ModelDB:149000" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 596, - "tag": "Synaptic Integration" - } - ], - "timestamp_created": "2024-01-12 09:40:18.066823+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/149000", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1202": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1202, - "name": "Preserving axosomatic spiking features despite diverse dendritic morphology (Hay et al., 2013)", - "repository_type": "github", - "summary": "The authors found that linearly scaling the ion channel conductance densities of a reference model with the conductance load in 28 3D reconstructed layer 5 thick-tufted pyramidal cells was necessary to match the experimental statistics of these cells electrical firing properties.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1735, - "tag": "ModelDB:149100" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 863, - "tag": "Parameter sensitivity" - } - ], - "timestamp_created": "2024-01-12 09:40:18.718769+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/149100", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1203": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1203, - "name": "Rescue of plasticity by a computationally predicted protocol (Liu et al. 2013)", - "repository_type": "github", - "summary": "\" ... A computational model,\r\nwhich simulated molecular processes underlying long-term\r\nsynaptic facilitation (LTF) induction, predicted a rescue protocol of five pulses of 5-HT at non-uniform interstimulus\r\nintervals that overcame the consequences of reduced CREB-binding protein (CBP) and restored LTF. ...\"", - "tags": [ - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1736, - "tag": "ModelDB:149162" - }, - { - "id": 751, - "tag": "Signaling pathways" - }, - { - "id": 759, - "tag": "XPPAUT" - } - ], - "timestamp_created": "2024-01-12 09:40:19.592910+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/149162", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1204": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1204, - "name": "State dependent drug binding to sodium channels in the dentate gyrus (Thomas & Petrou 2013)", - "repository_type": "github", - "summary": "A Markov model of sodium channels was developed that includes drug binding to fast inactivated states. This was incorporated into a model of the dentate gyrus to investigate the effects of anti-epileptic drugs on neuron and network properties.", - "tags": [ - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 1737, - "tag": "Drug binding" - }, - { - "id": 469, - "tag": "Epilepsy" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 1433, - "tag": "I_AHP" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 748, - "tag": "Markov-type model" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1738, - "tag": "ModelDB:149174" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:40:20.122932+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/149174", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1205": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1205, - "name": "Roles of essential kinases in induction of late hippocampal LTP (Smolen et al., 2006)", - "repository_type": "github", - "summary": "\"\u2026 Convergence of multiple kinase activities to induce L-LTP helps to generate a threshold whereby the amount of L-LTP varies steeply with the number of brief (tetanic) electrical stimuli. The model simulates tetanic, -burst, pairing-induced, and chemical L-LTP, as well as L-LTP due to synaptic tagging. The model also simulates inhibition of L-LTP by inhibition of MAPK, CAMKII, PKA, or CAMKIV. The model predicts results of experiments to delineate mechanisms underlying L-LTP induction and expression.\r\n\u2026\"\r\n", - "tags": [ - { - "id": 1739, - "tag": "CREB" - }, - { - "id": 778, - "tag": "Java" - }, - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1740, - "tag": "ModelDB:149175" - }, - { - "id": 751, - "tag": "Signaling pathways" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - } - ], - "timestamp_created": "2024-01-12 09:40:20.645227+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/149175", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1206": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1206, - "name": "Olfactory Computations in Mitral-Granule cell circuits (Migliore & McTavish 2013)", - "repository_type": "github", - "summary": "Model files for the entry \"Olfactory Computations in Mitral-Granule Cell Circuits\" of the Springer Encyclopedia of Computational Neuroscience by Michele Migliore and Tom Mctavish.\r\n\r\nThe simulations illustrate two typical Mitral-Granule cell circuits in the olfactory bulb of vertebrates: distance-independent lateral inhibition and gating effects.\r\n", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 763, - "tag": "Intrinsic plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1741, - "tag": "ModelDB:149415" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - } - ], - "timestamp_created": "2024-01-12 09:40:21.153779+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/149415", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1207": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 1207, - "name": "Modeling conductivity profiles in the deep neocortical pyramidal neuron (Wang K et al. 2013)", - "repository_type": "github", - "summary": "\"With the rapid increase in the number of technologies aimed at observing\r\nelectric activity inside the brain, scientists have felt the urge to create\r\nproper links between intracellular- and extracellular-based experimental\r\napproaches. Biophysical models at both physical scales have been formalized\r\nunder assumptions that impede the creation of such links. In this\r\nwork, we address this issue by proposing amulticompartment model that\r\nallows the introduction of complex extracellular and intracellular resistivity\r\nprofiles. This model accounts for the geometrical and electrotonic\r\nproperties of any type of neuron through the combination of four devices:\r\nthe integrator, the propagator, the 3D connector, and the collector. ...\"", - "tags": [ - { - "id": 1606, - "tag": "Conductance distributions" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 1546, - "tag": "Evoked LFP" - }, - { - "id": 734, - "tag": "Extracellular Fields" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 783, - "tag": "I_Ks" - }, - { - "id": 570, - "tag": "Influence of Dendritic Geometry" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1742, - "tag": "ModelDB:149737" - }, - { - "id": 743, - "tag": "NEURON (web link to model)" - } - ], - "timestamp_created": "2024-01-12 09:40:21.807111+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/149737", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1208": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1208, - "name": "A two-layer biophysical olfactory bulb model of cholinergic neuromodulation (Li and Cleland 2013)", - "repository_type": "github", - "summary": "This is a two-layer biophysical olfactory bulb (OB) network model to study cholinergic neuromodulation. Simulations show that nicotinic receptor activation sharpens mitral cell receptive field, while muscarinic receptor activation enhances network synchrony and gamma oscillations. This general model suggests that the roles of nicotinic and muscarinic receptors in OB are both distinct and complementary to one another, together regulating the effects of ascending cholinergic inputs on olfactory bulb transformations.", - "tags": [ - { - "id": 590, - "tag": "I A" - }, - { - "id": 731, - "tag": "I CAN" - }, - { - "id": 1560, - "tag": "I Ca,p" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 1531, - "tag": "I Cl, leak" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 783, - "tag": "I_Ks" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1743, - "tag": "ModelDB:149739" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1744, - "tag": "Neuromodulation" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 821, - "tag": "Sensory coding" - }, - { - "id": 758, - "tag": "Sensory processing" - } - ], - "timestamp_created": "2024-01-12 09:40:22.342327+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/149739", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1209": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1209, - "name": "Competing oscillator 5-cell circuit and Parameterscape plotting (Gutierrez et al. 2013)", - "repository_type": "github", - "summary": "Our 5-cell model consists of competing fast and slow oscillators connected to a hub neuron with electrical and inhibitory synapses. Motivated by the Stomatogastric Ganglion (STG) circuit in the crab, we explored the patterns of coordination in the network as a function of the electrical coupling and inhibitory synapse strengths with the help of a novel visualization method that we call the \"Parameterscape.\" The code submitted here will allow you to run circuit simulations and to produce a Parameterscape with the results.", - "tags": [ - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1745, - "tag": "ModelDB:149910" - }, - { - "id": 569, - "tag": "Simplified Models" - } - ], - "timestamp_created": "2024-01-12 09:40:22.877845+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/149910", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1210": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1210, - "name": "Distributed synaptic plasticity and spike timing (Garrido et al. 2013)", - "repository_type": "github", - "summary": "Here we have used a computational model to simulate the impact of multiple distributed synaptic weights in the cerebellar granular layer network. In response to mossy fiber bursts, synaptic weights at multiple connections played a crucial role to regulate spike number and positioning in granule cells. Interestingly, different combinations of synaptic weights optimized either first-spike timing precision or spike number, efficiently controlling transmission and filtering properties. These results predict that distributed synaptic plasticity regulates the emission of quasi-digital spike patterns on the millisecond time scale and allows the cerebellar granular layer to flexibly control burst transmission along the mossy fiber pathway.", - "tags": [ - { - "id": 1746, - "tag": "EDLUT" - }, - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1747, - "tag": "ModelDB:149913" - } - ], - "timestamp_created": "2024-01-12 09:40:23.471335+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/149913", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1211": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1211, - "name": "Prob. Inference of Short-Term Synaptic Plasticity in Neocort. Microcircuits (Costa et al. 2013)", - "repository_type": "github", - "summary": "\" ... As a solution (for Short Term Plasticity (STP) inference), we introduce a Bayesian formulation, which yields the posterior distribution over the model parameters given the data. First, we show that common STP protocols yield broad distributions over some model parameters. Using our result we propose a experimental protocol to more accurately determine synaptic dynamics parameters. Next, we infer the model parameters using experimental data from three different neocortical excitatory connection types. This reveals connection-specific distributions, which we use to classify synaptic dynamics. Our approach to demarcate connection-specific synaptic dynamics is an important improvement on the state of the art and reveals novel features from existing data.\"", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1748, - "tag": "ModelDB:149914" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - } - ], - "timestamp_created": "2024-01-12 09:40:23.978735+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/149914", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1212": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1212, - "name": "KV1 channel governs cerebellar output to thalamus (Ovsepian et al. 2013)", - "repository_type": "github", - "summary": "The output of the cerebellum to the motor axis of the central nervous system is\r\norchestrated mainly by synaptic inputs and intrinsic pacemaker activity of deep cerebellar nuclear\r\n(DCN) projection neurons. Herein, we demonstrate that the soma of these cells is enriched with\r\nKV1 channels produced by mandatory multi-merization of KV1.1, 1.2 alpha andKV beta2 subunits. Being\r\nconstitutively active, the K+ current (IKV1) mediated by these channels stabilizes the rate and\r\nregulates the temporal precision of self-sustained firing of these neurons. \r\n...\r\nThrough the use of multi-compartmental modelling and ... the physiological significance of the described functions for processing\r\nand communication of information from the lateral DCN to thalamic relay nuclei is established.", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 731, - "tag": "I CAN" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 783, - "tag": "I_Ks" - }, - { - "id": 763, - "tag": "Intrinsic plasticity" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1749, - "tag": "ModelDB:150024" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 573, - "tag": "Rebound firing" - } - ], - "timestamp_created": "2024-01-12 09:40:24.593440+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150024", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1213": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1213, - "name": "An attractor network model of grid cells and theta-nested gamma oscillations (Pastoll et al 2013)", - "repository_type": "github", - "summary": "A two population spiking continuous attractor model of grid cells. This model combines the attractor dynamics with theta-nested gamma oscillatory activity. It reproduces the behavioural response of grid cells (grid fields) in medial entorhinal cortex, while at the same time allowing for nested gamma oscillations of post-synaptic currents.", - "tags": [ - { - "id": 718, - "tag": "Attractor Neural Network" - }, - { - "id": 645, - "tag": "Brian" - }, - { - "id": 1461, - "tag": "Grid cell" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1750, - "tag": "ModelDB:150031" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 620, - "tag": "Python" - } - ], - "timestamp_created": "2024-01-12 09:40:25.141673+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150031", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1214": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1214, - "name": "Distributed cerebellar plasticity implements adaptable gain control (Garrido et al., 2013)", - "repository_type": "github", - "summary": "We tested the role of plasticity distributed over multiple synaptic sites (Hansel et al., 2001; Gao et al., 2012) by generating an analog cerebellar model embedded into a control loop connected to a robotic simulator. The robot used a three-joint arm and performed repetitive fast manipulations with different masses along an 8-shape trajectory. In accordance with biological evidence, the cerebellum model was endowed with both LTD and LTP at the PF-PC, MF-DCN and PC-DCN synapses. This resulted in a network scheme whose effectiveness was extended considerably compared to one including just PF-PC synaptic plasticity. Indeed, the system including distributed plasticity reliably self-adapted to manipulate different masses and to learn the arm-object dynamics over a time course that included fast learning and consolidation, along the lines of what has been observed in behavioral tests. In particular, PF-PC plasticity operated as a time correlator between the actual input state and the system error, while MF-DCN and PC-DCN plasticity played a key role in generating the gain controller. This model suggests that distributed synaptic plasticity allows generation of the complex learning properties of the cerebellum.", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1751, - "tag": "ModelDB:150067" - }, - { - "id": 824, - "tag": "Simulink" - } - ], - "timestamp_created": "2024-01-12 09:40:25.675424+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150067", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1215": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1215, - "name": "Biologically Constrained Basal Ganglia model (BCBG model) (Lienard, Girard 2014)", - "repository_type": "github", - "summary": "We studied the physiology and function of the basal ganglia through the design of mean-field models of the whole basal ganglia. The parameterizations are optimized with multi-objective evolutionary algorithm to respect best a collection of numerous anatomical data and electrophysiological data. The main outcomes of our study are: \u2022 The strength of the GPe to GPi/SNr connection does not support opposed activities in the GPe and GPi/SNr. \u2022 STN and MSN target more the GPe than the GPi/SNr. \u2022 Selection arises from the structure of the basal ganglia, without properly segregated direct and indirect pathways and without specific inputs from pyramidal tract neurons of the cortex. Selection is enhanced when the projection from GPe to GPi/SNr has a diffuse pattern.", - "tags": [ - { - "id": 771, - "tag": "Action Selection/Decision Making" - }, - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1752, - "tag": "ModelDB:150206" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 787, - "tag": "Pathophysiology" - }, - { - "id": 822, - "tag": "Winner-take-all" - } - ], - "timestamp_created": "2024-01-12 09:40:26.267481+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150206", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1216": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1216, - "name": "A Method for Prediction of Receptor Activation in the Simulation of Synapses (Montes et al. 2013)", - "repository_type": "github", - "summary": "A machine-learning based method that can accurately predict relevant aspects of the behavior of synapses, such as the activation of synaptic receptors, at very low computational cost. The method is designed to learn patterns and general principles from previous Monte Carlo simulations and to predict synapse behavior from them. The resulting procedure is accurate, automatic and can predict synapse behavior under experimental conditions that are different to the ones used during the learning phase. Since our method efficiently reduces the computational costs, it is suitable for the simulation of the vast number of synapses that occur in the mammalian brain.", - "tags": [ - { - "id": 778, - "tag": "Java" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1753, - "tag": "ModelDB:150207" - }, - { - "id": 1754, - "tag": "R" - }, - { - "id": 569, - "tag": "Simplified Models" - } - ], - "timestamp_created": "2024-01-12 09:40:26.763684+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150207", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1217": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1217, - "name": "Emergence of Connectivity Motifs in Networks of Model Neurons (Vasilaki, Giugliano 2014)", - "repository_type": "github", - "summary": "Recent evidence suggests that short-term dynamics of excitatory synaptic transmission is correlated to stereotypical connectivity motifs. \r\nWe show that these connectivity motifs emerge in networks of model neurons, from the interactions between short-term synaptic dynamics (SD) and long-term spike-timing dependent plasticity (STDP).", - "tags": [ - { - "id": 755, - "tag": "C or Cplusplus program" - }, - { - "id": 855, - "tag": "Connectivity matrix" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1755, - "tag": "ModelDB:150211" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 569, - "tag": "Simplified Models" - } - ], - "timestamp_created": "2024-01-12 09:40:27.256093+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150211", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1218": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1218, - "name": "Reliability of Morris-Lecar neurons with added T, h, and AHP currents (Zeldenrust et al. 2013)", - "repository_type": "github", - "summary": "We investigated the reliability of the timing of spikes in a spike train in a Morris-Lecar model with several extensions. A frozen Gaussian noise current, superimposed on a DC current, was injected. The neuron responded with spike trains that showed trial-to-trial variability. The reliability depends on the shape (steepness) of the current input versus spike frequency output curve. The model also allowed to study the contribution of three relevant ionic membrane currents to reliability: a T-type calcium current, a cation selective h-current and a calcium dependent potassium current in order to allow bursting, investigate the consequences of a more complex current-frequency relation and produce realistic firing rates.", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 1433, - "tag": "I_AHP" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1756, - "tag": "ModelDB:150217" - }, - { - "id": 862, - "tag": "Reliability" - }, - { - "id": 759, - "tag": "XPPAUT" - } - ], - "timestamp_created": "2024-01-12 09:40:27.771609+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150217", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1219": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1219, - "name": "Fast convergence of cerebellar learning (Luque et al. 2015)", - "repository_type": "github", - "summary": "The cerebellum is known to play a critical role in learning relevant patterns of activity for adaptive motor control, but the underlying network mechanisms are only partly understood. The classical long-term synaptic plasticity between parallel fibers (PFs) and Purkinje cells (PCs), which is driven by the inferior olive (IO), can only account for limited aspects of learning. Recently, the role of additional forms of plasticity in the granular layer, molecular layer and deep cerebellar nuclei (DCN) has been considered. In particular, learning at DCN synapses allows for generalization, but convergence to a stable state requires hundreds of repetitions. In this paper we have explored the putative role of the IO-DCN connection by endowing it with adaptable weights and exploring its implications in a closed-loop robotic manipulation task. Our results show that IO-DCN plasticity accelerates convergence of learning by up to two orders of magnitude without conflicting with the generalization properties conferred by DCN plasticity. Thus, this model suggests that multiple distributed learning mechanisms provide a key for explaining the complex properties of procedural learning and open up new experimental questions for synaptic plasticity in the cerebellar network.", - "tags": [ - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1757, - "tag": "ModelDB:150225" - }, - { - "id": 802, - "tag": "STDP" - }, - { - "id": 824, - "tag": "Simulink" - } - ], - "timestamp_created": "2024-01-12 09:40:28.291262+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150225", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1220": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1220, - "name": "MEC layer II stellate cell: Synaptic mechanisms of grid cells (Schmidt-Hieber & Hausser 2013)", - "repository_type": "github", - "summary": "This study investigates the cellular mechanisms of grid field generation in Medial Entorhinal Cortex (MEC) layer II stellate cells.", - "tags": [ - { - "id": 718, - "tag": "Attractor Neural Network" - }, - { - "id": 1461, - "tag": "Grid cell" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 766, - "tag": "I CNG" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1758, - "tag": "ModelDB:150239" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 799, - "tag": "Place cell/field" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 800, - "tag": "Spatial Navigation" - }, - { - "id": 596, - "tag": "Synaptic Integration" - } - ], - "timestamp_created": "2024-01-12 09:40:28.857267+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150239", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1221": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1221, - "name": "A Model Circuit of Thalamocortical Convergence (Behuret et al. 2013)", - "repository_type": "github", - "summary": "\u201c\u2026\r\nUsing dynamic-clamp techniques in thalamic slices in vitro, we combined theoretical and experimental\r\napproaches to implement a realistic hybrid retino-thalamo-cortical pathway mixing biological cells and simulated circuits.\r\n\u2026\r\nThe study of\r\nthe impact of the simulated cortical input on the global retinocortical signal transfer efficiency revealed a novel control\r\nmechanism resulting from the collective resonance of all thalamic relay neurons. \r\nWe show here that the transfer efficiency\r\nof sensory input transmission depends on three key features: i) the number of thalamocortical cells involved in the many-to-one\r\nconvergence from thalamus to cortex, ii) the statistics of the corticothalamic synaptic bombardment and iii) the level of\r\ncorrelation imposed between converging thalamic relay cells. \r\nIn particular, our results demonstrate counterintuitively that\r\nthe retinocortical signal transfer efficiency increases when the level of correlation across thalamic cells decreases. \r\n\u2026\u201d\r\n", - "tags": [ - { - "id": 576, - "tag": "I K" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1759, - "tag": "ModelDB:150240" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 768, - "tag": "Synaptic Convergence" - } - ], - "timestamp_created": "2024-01-12 09:40:29.416582+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150240", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1222": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1222, - "name": "Sensorimotor cortex reinforcement learning of 2-joint virtual arm reaching (Neymotin et al. 2013)", - "repository_type": "github", - "summary": "\"...\r\nWe developed a model of sensory and motor neocortex consisting\r\nof 704 spiking model-neurons. Sensory and motor populations included excitatory cells\r\nand two types of interneurons. Neurons were interconnected with AMPA/NMDA, and\r\nGABAA synapses. We trained our model using spike-timing-dependent reinforcement\r\nlearning to control a 2-joint virtual arm to reach to a fixed target. \r\n...\r\n\"", - "tags": [ - { - "id": 825, - "tag": "Learning" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1760, - "tag": "ModelDB:150245" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 809, - "tag": "Reinforcement Learning" - }, - { - "id": 859, - "tag": "Reward-modulated STDP" - }, - { - "id": 802, - "tag": "STDP" - }, - { - "id": 758, - "tag": "Sensory processing" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - } - ], - "timestamp_created": "2024-01-12 09:40:29.948519+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150245", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1223": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1223, - "name": "Multiscale simulation of the striatal medium spiny neuron (Mattioni & Le Novere 2013)", - "repository_type": "github", - "summary": "\"\u2026 We present a new event-driven algorithm to synchronize different neuronal\r\nmodels, which decreases computational time and avoids superfluous synchronizations.\r\n\r\nThe algorithm is implemented in the TimeScales framework. \r\n\r\nWe demonstrate its use by simulating a new multiscale model of the Medium Spiny Neuron of the Neostriatum.\r\n\r\nThe model comprises over a thousand dendritic spines, where the electrical model interacts with the\r\nrespective instances of a biochemical model.\r\n\r\nOur results show that a multiscale model is able to exhibit changes of synaptic\r\nplasticity as a result of the interaction between electrical and biochemical signaling.\r\n\u2026\"", - "tags": [ - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 731, - "tag": "I CAN" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 842, - "tag": "I Krp" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 843, - "tag": "I Q" - }, - { - "id": 844, - "tag": "I R" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1761, - "tag": "ModelDB:150284" - }, - { - "id": 866, - "tag": "Multiscale" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 751, - "tag": "Signaling pathways" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - } - ], - "timestamp_created": "2024-01-12 09:40:30.488878+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150284", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1224": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1224, - "name": "A 1000 cell network model for Lateral Amygdala (Kim et al. 2013)", - "repository_type": "github", - "summary": "1000 Cell Lateral Amygdala model for investigation of plasticity and memory storage during Pavlovian Conditioning.", - "tags": [ - { - "id": 1684, - "tag": "Ca pump" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 1433, - "tag": "I_AHP" - }, - { - "id": 825, - "tag": "Learning" - }, - { - "id": 749, - "tag": "Long-term Synaptic Plasticity" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1762, - "tag": "ModelDB:150288" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1744, - "tag": "Neuromodulation" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - } - ], - "timestamp_created": "2024-01-12 09:40:31.027751+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150288", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1225": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1225, - "name": "Network bursts in cultured NN result from different adaptive mechanisms (Masquelier & Deco 2013)", - "repository_type": "github", - "summary": "It is now well established that cultured neuron networks are spontaneously active, and tend to synchronize. Synchronous events typically involve the whole network, and have thus been termed \u201cnetwork spikes\u201d (NS). Using experimental recordings and numerical simulations, we show here that the inter-NS interval statistics are complex, and allow inferring the neural mechanisms at work, in particular the adaptive ones, and estimating a number of parameters to which we cannot access experimentally.", - "tags": [ - { - "id": 645, - "tag": "Brian" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 722, - "tag": "Depression" - }, - { - "id": 723, - "tag": "Facilitation" - }, - { - "id": 1433, - "tag": "I_AHP" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1763, - "tag": "ModelDB:150437" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - }, - { - "id": 777, - "tag": "Spike Frequency Adaptation" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - } - ], - "timestamp_created": "2024-01-12 09:40:31.593660+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150437", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1226": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1226, - "name": "Spontaneous weakly correlated excitation and inhibition (Tan et al. 2013)", - "repository_type": "github", - "summary": "Brian code for Tan et al. 2013.", - "tags": [ - { - "id": 645, - "tag": "Brian" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1764, - "tag": "ModelDB:150440" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 726, - "tag": "Vision" - } - ], - "timestamp_created": "2024-01-12 09:40:32.193886+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150440", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1227": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1227, - "name": "Interaural time difference detection by slowly integrating neurons (Vasilkov Tikidji-Hamburyan 2012)", - "repository_type": "github", - "summary": "For localization of a sound source, animals and humans process the microsecond interaural time differences of arriving sound waves. How nervous systems, consisting of elements with time constants of about and more than 1 ms, can reach such high precision is still an open question. This model shows that population of 10000 slowly integrating Hodgkin-Huxley neurons with inhibitory and excitatory inputs (EI neurons) can detect minute temporal disparities in input signals which are significantly less than any time constant in the system.", - "tags": [ - { - "id": 762, - "tag": "Audition" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1765, - "tag": "ModelDB:150445" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:40:32.673407+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150445", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1228": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1228, - "name": "Synaptic gating at axonal branches, and sharp-wave ripples with replay (Vladimirov et al. 2013)", - "repository_type": "github", - "summary": "The computational model of in vivo sharp-wave ripples with place cell replay. Excitatory post-synaptic potentials at dendrites gate antidromic spikes arriving from the axonal collateral, and thus determine when the soma and the main axon fire. The model allows synchronous replay of pyramidal cells during sharp-wave ripple event, and the replay is possible in both forward and reverse directions.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 728, - "tag": "Axonal Action Potentials" - }, - { - "id": 1527, - "tag": "Brain Rhythms" - }, - { - "id": 745, - "tag": "Conduction failure" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 1433, - "tag": "I_AHP" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1766, - "tag": "ModelDB:150446" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 799, - "tag": "Place cell/field" - }, - { - "id": 586, - "tag": "Synchronization" - } - ], - "timestamp_created": "2024-01-12 09:40:33.231830+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150446", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1229": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1229, - "name": "L5 pyr. cell spiking control by oscillatory inhibition in distal apical dendrites (Li et al 2013)", - "repository_type": "github", - "summary": "This model examined how distal oscillatory inhibition influences the firing of a biophysically-detailed layer 5 pyramidal neuron model.", - "tags": [ - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 1532, - "tag": "I Na, leak" - }, - { - "id": 763, - "tag": "Intrinsic plasticity" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1767, - "tag": "ModelDB:150538" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:40:33.809979+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150538", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1230": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1230, - "name": "Calcium waves and mGluR-dependent synaptic plasticity in CA1 pyr. neurons (Ashhad & Narayanan 2013)", - "repository_type": "github", - "summary": "A morphologically realistic, conductance-based model equipped with kinetic schemes that govern several calcium signalling modules and pathways in CA1 pyramidal neurons", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 1684, - "tag": "Ca pump" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 790, - "tag": "Calcium waves" - }, - { - "id": 781, - "tag": "G-protein coupled" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1768, - "tag": "ModelDB:150551" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 751, - "tag": "Signaling pathways" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - } - ], - "timestamp_created": "2024-01-12 09:40:34.339989+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150551", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1231": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1231, - "name": "Single compartment Dorsal Lateral Medium Spiny Neuron w/ NMDA and AMPA (Biddell and Johnson 2013)", - "repository_type": "github", - "summary": "A biophysical single compartment model of the dorsal lateral striatum medium spiny neuron is presented here. The model is an implementation then adaptation of a previously described model (Mahon et al. 2002). The model has been adapted to include NMDA and AMPA receptor models that have been fit to dorsal lateral striatal neurons. The receptor models allow for excitation by other neuron models.", - "tags": [ - { - "id": 831, - "tag": "Deep brain stimulation" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 842, - "tag": "I Krp" - }, - { - "id": 1532, - "tag": "I Na, leak" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 783, - "tag": "I_Ks" - }, - { - "id": 825, - "tag": "Learning" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1769, - "tag": "ModelDB:150556" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 801, - "tag": "Parkinson's" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - } - ], - "timestamp_created": "2024-01-12 09:40:34.871447+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150556", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1232": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1232, - "name": "Striatal Output Neuron (Mahon, Deniau, Charpier, Delord 2000)", - "repository_type": "github", - "summary": "Striatal output neurons (SONs) integrate glutamatergic synaptic inputs originating from the cerebral cortex. In vivo electrophysiological data have shown that a prior depolarization of SONs induced a short-term (1 sec)increase in their membrane excitability, which facilitated the ability of corticostriatal synaptic potentials to induce firing. Here we propose, using a computational model of SONs, that the use-dependent, short-term increase in the responsiveness of SONs mainly results from the slow kinetics of a voltage-dependent, slowly inactivating potassium A-current. This mechanism confers on SONs a form of intrinsic short-term memory that optimizes the synaptic input\u00e2\u20ac\u201coutput relationship as a function of their past activation.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 842, - "tag": "I Krp" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 783, - "tag": "I_Ks" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1770, - "tag": "ModelDB:150621" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 724, - "tag": "Short-term Synaptic Plasticity" - } - ], - "timestamp_created": "2024-01-12 09:40:35.411584+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150621", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1233": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1233, - "name": "Revised opponent-channel model of auditory space cortical representation (Briley & Summerfield 2013)", - "repository_type": "github", - "summary": "This is the computational opponent-channel model used by Briley et al. (2013) to model electroencephalographic (EEG) responses from the auditory cortices of young, younger-old and older-old adults to abrupt shifts in sound-source location, and to predict each groups' psychophysical measures of spatial acuity.", - "tags": [ - { - "id": 762, - "tag": "Audition" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1771, - "tag": "ModelDB:150622" - }, - { - "id": 758, - "tag": "Sensory processing" - } - ], - "timestamp_created": "2024-01-12 09:40:35.997014+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150622", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1234": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 1234, - "name": "Stochastic calcium mechanisms cause dendritic calcium spike variability (Anwar et al. 2013)", - "repository_type": "github", - "summary": "\" ...\r\nIn single Purkinje cells, spontaneous and synaptically evoked\r\ndendritic calcium bursts come in a variety of shapes with a variable number of\r\nspikes. \r\n\r\nThe mechanisms causing this variability have never been investigated thoroughly.\r\n\r\nIn this study, a detailed computational model employing novel simulation routines is\r\napplied to identify the roles that stochastic ion channels, spatial arrangements of ion\r\nchannels and stochastic intracellular calcium have towards producing calcium burst\r\nvariability.\r\n\u2026\r\nOur findings suggest that stochastic intracellular calcium\r\nmechanisms play a crucial role in dendritic calcium spike generation and are, therefore, an\r\nessential consideration in studies of neuronal excitability and plasticity.\"", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 1684, - "tag": "Ca pump" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 742, - "tag": "I p,q" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1772, - "tag": "ModelDB:150635" - }, - { - "id": 1773, - "tag": "STEPS (web link to model)" - } - ], - "timestamp_created": "2024-01-12 09:40:36.553568+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150635", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1235": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1235, - "name": "Auditory cortex layer IV network model (Beeman 2013)", - "repository_type": "github", - "summary": "\"... The primary objective of this modeling study was to determine the effects of axonal conduction velocity (often neglected, but significant), as well as synaptic time constants, on the ability of such a network to create and propagate cortical waves. ... The model is also being used to study the interaction between single and two-tone input and normal background activity, and the effects of synaptic depression from thalamic inputs. The simulation scripts have the additional purpose of serving as tutorial examples for the construction of cortical networks with GENESIS. The present model has fostered the development of the G-3 Python network analysis and visualization tools used in this study... It is my hope that this short tutorial and the example\r\nsimulation scripts can provide a head start for a graduate student or\r\npostdoc who is beginning a cortical modeling project.\r\n\"", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1774, - "tag": "ModelDB:150678" - }, - { - "id": 732, - "tag": "Tutorial/Teaching" - } - ], - "timestamp_created": "2024-01-12 09:40:37.202348+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150678", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1236": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1236, - "name": "Model of arrhythmias in a cardiac cells network (Casaleggio et al. 2014)", - "repository_type": "github", - "summary": "\" ... Here we explore the possible processes leading to the occasional onset and termination of the (usually) non-fatal arrhythmias widely observed in the heart.\r\n\r\nUsing a computational model of a two-dimensional network of cardiac cells, we tested the hypothesis that an ischemia alters the properties of the gap junctions inside the ischemic area.\r\n...\r\n In conclusion, our model strongly supports the hypothesis that non-fatal arrhythmias can develop from post-ischemic alteration of the electrical connectivity in a relatively small area of the cardiac cell network, and suggests experimentally testable predictions on their possible treatments.\"", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 1606, - "tag": "Conductance distributions" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 737, - "tag": "Heart disease" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1775, - "tag": "ModelDB:150691" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - } - ], - "timestamp_created": "2024-01-12 09:40:37.758894+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150691", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1237": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1237, - "name": "Crayfish hybrid experimental model (Chung et al. 2015)", - "repository_type": "github", - "summary": "The Crayfish hybrid experimental model is an AnimatLab v1 neuromechanical model of the crayfish thorax and 5th walking leg that provides a virtual periphery to the live crayfish central nervous system. Run in real time, levator and depressor muscles are excited by motor nerve discharges of the CNS. Up and down movement of the leg shortens and stretches a model stretch receptor that controls movement of a real stretch receptor that provides sensory feedback to the CNS. Real-time sensory feedback provided by the model increases the locomotor cycle frequency by three-fold.", - "tags": [ - { - "id": 1776, - "tag": "AnimatLab v1" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1777, - "tag": "ModelDB:150697" - } - ], - "timestamp_created": "2024-01-12 09:40:38.320978+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150697", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1238": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1238, - "name": "Crayfish hybrid simulation model (Bacque-Cazenave et al. 2014)", - "repository_type": "github", - "summary": "A neuromechanical model of the crayfish leg and thorax and the postural and locomotor circuitry built and run in AnimatLab v1. The model simulates experiments run with the BCI preparation model in which the model was linked in real time to the in vivo crayfish thoracic nerve cord. The model shows that current understanding of the neural circuitry can account for the increase in locomotor frequency when the sensori-motor feedback loop is intact.", - "tags": [ - { - "id": 1776, - "tag": "AnimatLab v1" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1778, - "tag": "ModelDB:150698" - }, - { - "id": 1779, - "tag": "Posture and locomotion" - } - ], - "timestamp_created": "2024-01-12 09:40:39.008536+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150698", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1239": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1239, - "name": "Computational modelling of channelrhodopsin-2 photocurrent characteristics (Stefanescu et al. 2013)", - "repository_type": "github", - "summary": "The codes are directly related with the results presented in the manuscript; in brief, it is a computational investigation on the effects of optogenetic actuation on excitatory and inhibitory neurons when 3- and 4- state model is used to implement the ChR2 kinetics. Different parameters of optostimulation are investigated and the results compared with experimental data previously published by other research groups.", - "tags": [ - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1780, - "tag": "ModelDB:150804" - } - ], - "timestamp_created": "2024-01-12 09:40:39.537011+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150804", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1240": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1240, - "name": "Dynamic cortical interlaminar interactions (Carracedo et al. 2013)", - "repository_type": "github", - "summary": "\"... Here we demonstrate the mechanism underlying a purely neocortical delta rhythm generator and show a remarkable laminar, cell subtype and local subcircuit delineation between delta\r\nand nested theta rhythms. We show that spike timing during delta-nested theta rhythms controls an iterative, reciprocal interaction between deep and superficial cortical layers resembling the unsupervised learning processes proposed for laminar neural networks by Hinton and colleagues ... and mimicking the alternating cortical dynamics of sensory and memory processing during wakefulness.\"\r\n", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 710, - "tag": "FORTRAN" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1781, - "tag": "ModelDB:150806" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 592, - "tag": "Sleep" - } - ], - "timestamp_created": "2024-01-12 09:40:40.073465+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150806", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1241": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1241, - "name": "Grid cells from place cells (Castro & Aguiar, 2014)", - "repository_type": "github", - "summary": "\" ...Here we present a novel model for the emergence of gridlike firing patterns that stands on two key hypotheses: (1) spatial information in GCs is provided from PC activity and (2) grid fields result from a combined synaptic plasticity mechanism involving inhibitory and excitatory neurons mediating the connections between PCs and GCs. ...\"", - "tags": [ - { - "id": 1461, - "tag": "Grid cell" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1782, - "tag": "ModelDB:150846" - }, - { - "id": 799, - "tag": "Place cell/field" - }, - { - "id": 791, - "tag": "Rate-coding model neurons" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - } - ], - "timestamp_created": "2024-01-12 09:40:40.590675+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150846", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1242": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1242, - "name": "Calcium influx during striatal upstates (Evans et al. 2013)", - "repository_type": "github", - "summary": "\"...\r\nTo\r\ninvestigate the mechanisms that underlie the relationship between\r\ncalcium and AP timing, we have developed a realistic biophysical\r\nmodel of a medium spiny neuron (MSN).\r\n...\r\nUsing this model, we found that either the slow inactivation of\r\ndendritic sodium channels (NaSI) or the calcium inactivation of\r\nvoltage-gated calcium channels (CDI) can cause high calcium corresponding\r\nto early APs and lower calcium corresponding to later APs.\r\n\r\nWe found that only CDI can account for the experimental observation\r\nthat sensitivity to AP timing is dependent on NMDA receptors.\r\n\r\nAdditional simulations demonstrated a mechanism by which MSNs\r\ncan dynamically modulate their sensitivity to AP timing and show that\r\nsensitivity to specifically timed pre- and postsynaptic pairings (as in\r\nspike timing-dependent plasticity protocols) is altered by the timing of\r\nthe pairing within the upstate.\r\n\u2026\"", - "tags": [ - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 842, - "tag": "I Krp" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 844, - "tag": "I R" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1783, - "tag": "ModelDB:150912" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 802, - "tag": "STDP" - } - ], - "timestamp_created": "2024-01-12 09:40:41.102792+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/150912", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1243": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1243, - "name": "Effects of increasing CREB on storage and recall processes in a CA1 network (Bianchi et al. 2014)", - "repository_type": "github", - "summary": "Several recent results suggest that boosting the CREB pathway improves hippocampal-dependent memory in healthy rodents and restores this type of\r\nmemory in an AD mouse model. However, not much is known about how CREB-dependent neuronal alterations in synaptic strength, excitability and\r\nLTP can boost memory formation in the complex architecture of a neuronal network. Using a model of a CA1 microcircuit, we investigate whether\r\nhippocampal CA1 pyramidal neuron properties altered by increasing CREB activity may contribute to improve memory storage and recall. With a set of patterns presented to a network, we find that the pattern recall quality under AD-like conditions is significantly better when boosting CREB function with respect to control. The results are robust and consistent upon increasing the synaptic damage expected by AD progression, supporting the idea that the use of CREB-based therapies could provide a new approach\r\nto treat AD.", - "tags": [ - { - "id": 827, - "tag": "Aging/Alzheimer`s" - }, - { - "id": 1739, - "tag": "CREB" - }, - { - "id": 1684, - "tag": "Ca pump" - }, - { - "id": 873, - "tag": "Depolarization block" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 1531, - "tag": "I Cl, leak" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 1433, - "tag": "I_AHP" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1784, - "tag": "ModelDB:151126" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 802, - "tag": "STDP" - }, - { - "id": 1377, - "tag": "Storage/recall" - } - ], - "timestamp_created": "2024-01-12 09:40:41.653533+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/151126", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1244": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1244, - "name": "Ih tunes oscillations in an In Silico CA3 model (Neymotin et al. 2013)", - "repository_type": "github", - "summary": "\" ... We investigated oscillatory control using a multiscale computer model of hippocampal CA3, where each cell class\r\n(pyramidal, basket, and oriens-lacunosum moleculare cells), contained type-appropriate isoforms of Ih.\r\n\r\nOur model\r\ndemonstrated that modulation of pyramidal and basket Ih allows tuning theta and gamma oscillation frequency and\r\namplitude. Pyramidal Ih also controlled cross-frequency coupling (CFC) and allowed shifting gamma generation towards\r\nparticular phases of the theta cycle, effected via Ih\u2019s ability to set pyramidal excitability. ...\"", - "tags": [ - { - "id": 1527, - "tag": "Brain Rhythms" - }, - { - "id": 1606, - "tag": "Conductance distributions" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1785, - "tag": "ModelDB:151282" - }, - { - "id": 866, - "tag": "Multiscale" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 620, - "tag": "Python" - } - ], - "timestamp_created": "2024-01-12 09:40:42.216906+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/151282", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1245": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1245, - "name": "Gating of steering signals through phasic modulation of reticulospinal neurons (Kozlov et al. 2014)", - "repository_type": "github", - "summary": "\" ... We use the lamprey as a model for investigating the role of this\r\nphasic modulation of the reticulospinal activity, because the\r\nbrainstem\u2013spinal cord networks are known down to the cellular level in\r\nthis phylogenetically oldest extant vertebrate.\r\n\r\nWe describe how the phasic modulation of reticulospinal activity from\r\nthe spinal CPG ensures reliable steering/turning commands without the\r\nneed for a very precise timing of on- or offset, by using a\r\nbiophysically detailed large-scale (19,600 model neurons and 646,800\r\nsynapses) computational model of the lamprey brainstem\u2013spinal cord\r\nnetwork.\r\n\r\nTo verify that the simulated neural network can control body\r\nmovements, including turning, the spinal activity is fed to a\r\nmechanical model of lamprey swimming.\r\n...\"", - "tags": [ - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 600, - "tag": "GENESIS" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 783, - "tag": "I_Ks" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1786, - "tag": "ModelDB:151338" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 777, - "tag": "Spike Frequency Adaptation" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - } - ], - "timestamp_created": "2024-01-12 09:40:42.772032+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/151338", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1246": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1246, - "name": "Excitatory synaptic interactions in pyramidal neuron dendrites (Behabadi et al. 2012)", - "repository_type": "github", - "summary": "\" ...\r\nWe hypothesized that if two excitatory pathways bias their synaptic projections towards proximal vs. distal ends of the basal branches, the very different local spike thresholds and attenuation factors for inputs near and far from the soma might provide the basis for a classical-contextual functional asymmetry. Supporting this possibility, we found both in compartmental models and electrophysiological recordings in brain slices that the responses of basal dendrites to spatially separated inputs are indeed strongly asymmetric.\r\n...\"", - "tags": [ - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1787, - "tag": "ModelDB:151404" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 587, - "tag": "Spatio-temporal Activity Patterns" - }, - { - "id": 596, - "tag": "Synaptic Integration" - } - ], - "timestamp_created": "2024-01-12 09:40:43.308189+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/151404", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1247": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1247, - "name": "Recurrent discharge in a reduced model of cat spinal motoneuron (Balbi et al, 2013)", - "repository_type": "github", - "summary": "Following a distal stimulation of a motor fibre, only a fraction of spinal motoneurons are able to produce a re-excitation of the initial segment leading to an orthodromically conducted action potential, known as recurrent discharge. In order to show the reciprocal interplay of the axonal initial segment and the soma leading to recurrent discharge in detail, a reduced model of a cat spinal motoneuron was developed.", - "tags": [ - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1788, - "tag": "ModelDB:151443" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 1789, - "tag": "Recurrent Discharge" - } - ], - "timestamp_created": "2024-01-12 09:40:43.881756+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/151443", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1248": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1248, - "name": "Calcium response prediction in the striatal spines depending on input timing (Nakano et al. 2013)", - "repository_type": "github", - "summary": "We construct an electric compartment model of the striatal medium spiny neuron with a realistic morphology and predict the calcium responses in the synaptic spines with variable timings of the glutamatergic and dopaminergic inputs and the postsynaptic action potentials.\r\nThe model was validated by reproducing the responses to current inputs and could predict the electric and calcium responses to glutamatergic inputs and back-propagating action potential in the proximal and distal synaptic spines during up and down states.", - "tags": [ - { - "id": 1684, - "tag": "Ca pump" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 730, - "tag": "I A, slow" - }, - { - "id": 731, - "tag": "I CAN" - }, - { - "id": 1560, - "tag": "I Ca,p" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 842, - "tag": "I Krp" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 1532, - "tag": "I Na, leak" - }, - { - "id": 739, - "tag": "I Na,p" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 843, - "tag": "I Q" - }, - { - "id": 844, - "tag": "I R" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1790, - "tag": "ModelDB:151458" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 809, - "tag": "Reinforcement Learning" - }, - { - "id": 859, - "tag": "Reward-modulated STDP" - }, - { - "id": 802, - "tag": "STDP" - } - ], - "timestamp_created": "2024-01-12 09:40:44.572782+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/151458", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1249": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1249, - "name": "DBS of a multi-compartment model of subthalamic nucleus projection neurons (Miocinovic et al. 2006)", - "repository_type": "github", - "summary": "We built a comprehensive computational model of subthalamic nucleus (STN) deep brain stimulation (DBS) in parkinsonian macaques to study the effects of stimulation in a controlled environment. The model consisted of three fundamental components: 1) a three-dimensional (3D) anatomical model of the macaque basal ganglia, 2) a finite element model of the DBS electrode and electric field transmitted to the tissue medium, and 3) multicompartment biophysical models of STN projection neurons, GPi fibers of passage, and internal capsule fibers of passage. Populations of neurons were positioned within the 3D anatomical model. Neurons were stimulated with electrode positions and stimulation parameters defined as clinically effective in two parkinsonian monkeys. The model predicted axonal activation of STN neurons and GPi fibers during STN DBS. Model predictions regarding the degree of GPi fiber activation matched well with experimental recordings in both monkeys.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 831, - "tag": "Deep brain stimulation" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 1532, - "tag": "I Na, leak" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1791, - "tag": "ModelDB:151460" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 801, - "tag": "Parkinson's" - } - ], - "timestamp_created": "2024-01-12 09:40:45.246370+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/151460", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1250": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1250, - "name": "Sympathetic Preganglionic Neurone (Briant et al. 2014)", - "repository_type": "github", - "summary": "A model of a sympathetic preganglionic neurone of muscle vasoconstrictor-type.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 589, - "tag": "I L high threshold" - }, - { - "id": 721, - "tag": "I N" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 1433, - "tag": "I_AHP" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1792, - "tag": "ModelDB:151482" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 568, - "tag": "Parameter Fitting" - }, - { - "id": 863, - "tag": "Parameter sensitivity" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - } - ], - "timestamp_created": "2024-01-12 09:40:45.848718+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/151482", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1251": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1251, - "name": "Simulating ion channel noise in an auditory brainstem neuron model (Schmerl & McDonnell 2013)", - "repository_type": "github", - "summary": "\" ... Here we demonstrate that biophysical models of channel noise can give rise to two\r\nkinds of recently discovered stochastic facilitation effects in a Hodgkin-Huxley-like model of auditory brainstem\r\nneurons. The first, known as slope-based stochastic resonance (SBSR), enables phasic neurons to emit action\r\npotentials that can encode the slope of inputs that vary slowly relative to key time constants in the model. \r\n\r\nThe\r\nsecond, known as inverse stochastic resonance (ISR), occurs in tonically firing neurons when small levels of\r\nnoise inhibit tonic firing and replace it with burstlike dynamics. ...\"\r\nPreprint available at http://arxiv.org/abs/1311.2643", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 762, - "tag": "Audition" - }, - { - "id": 804, - "tag": "Bifurcation" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1793, - "tag": "ModelDB:151483" - }, - { - "id": 874, - "tag": "Noise Sensitivity" - } - ], - "timestamp_created": "2024-01-12 09:40:46.458326+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/151483", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1252": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "main", - "id": 1252, - "name": "Voltage and light-sensitive Channelrhodopsin-2 model (ChR2) (Williams et al. 2013)", - "repository_type": "github", - "summary": "\" ... \r\nFocusing on one of the most widely used ChR2 mutants\r\n(H134R) with enhanced current, we collected a comprehensive\r\nexperimental data set of the response of this ion channel to different\r\nirradiances and voltages, and used these data to develop a model of\r\nChR2 with empirically-derived voltage- and irradiance- dependence,\r\nwhere parameters were fine-tuned via simulated annealing\r\noptimization.\r\n\r\nThis ChR2 model offers: 1) accurate inward rectification\r\nin the current-voltage response across irradiances; 2)\r\nempirically-derived voltage- and light-dependent kinetics (activation,\r\ndeactivation and recovery from inactivation); and 3) accurate\r\namplitude and morphology of the response across voltage and irradiance\r\nsettings. \r\n\r\nTemperature-scaling factors (Q10) were derived and model kinetics was\r\nadjusted to physiological temperatures.\r\n... \"", - "tags": [ - { - "id": 1794, - "tag": "Channelrhodopsin (ChR)" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 767, - "tag": "MATLAB (web link to model)" - }, - { - "id": 756, - "tag": "Methods" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1795, - "tag": "ModelDB:151549" - } - ], - "timestamp_created": "2024-01-12 09:40:46.977864+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/151549", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1253": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1253, - "name": "Tapered whiskers are required for active tactile sensation (Hires et al. 2013)", - "repository_type": "github", - "summary": "\" ... \r\nThe diverse shapes of facial whiskers reflect distinct ecological niches. Rodent whiskers\r\nare conical, often with a remarkably linear taper. \r\n\r\nHere we use theoretical and experimental methods\r\nto analyze interactions of mouse whiskers with objects. \r\n... \"\r\n\r\nThis is a quasi-static solution of the bending of an isolated whisker.\r\n\r\nFor Fig. 2, stable solution, use: theta=-0.174533 rad.\r\n\r\nUse \"Bndryval -> Show\" in XPPAUT.\r\n", - "tags": [ - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1796, - "tag": "ModelDB:151677" - }, - { - "id": 759, - "tag": "XPPAUT" - } - ], - "timestamp_created": "2024-01-12 09:40:47.769221+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/151677", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1254": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1254, - "name": "3D model of the olfactory bulb (Migliore et al. 2014)", - "repository_type": "github", - "summary": "This entry contains a link to a full HD version of movie 1 and the NEURON code of the paper:\r\n\"Distributed organization of a brain microcircuit analysed by three-dimensional modeling: the olfactory bulb\" by M Migliore, F Cavarretta, ML Hines, and GM Shepherd.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 566, - "tag": "Bursting" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1797, - "tag": "ModelDB:151681" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 719, - "tag": "Pattern Recognition" - }, - { - "id": 596, - "tag": "Synaptic Integration" - }, - { - "id": 725, - "tag": "Synaptic Plasticity" - }, - { - "id": 586, - "tag": "Synchronization" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 803, - "tag": "Unsupervised Learning" - } - ], - "timestamp_created": "2024-01-12 09:40:48.286205+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/151681", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1255": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1255, - "name": "Current Dipole in Laminar Neocortex (Lee et al. 2013)", - "repository_type": "github", - "summary": "Laminar neocortical model in NEURON/Python, adapted from Jones et al 2009.\r\n\r\nhttps://bitbucket.org/jonescompneurolab/corticaldipole", - "tags": [ - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 1798, - "tag": "Current Dipole" - }, - { - "id": 1799, - "tag": "Gamma oscillations" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 580, - "tag": "I M" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 575, - "tag": "I T low threshold" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 851, - "tag": "Magnetoencephalography" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1800, - "tag": "ModelDB:151685" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 743, - "tag": "NEURON (web link to model)" - }, - { - "id": 585, - "tag": "Oscillations" - }, - { - "id": 620, - "tag": "Python" - }, - { - "id": 853, - "tag": "Python (web link to model)" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - }, - { - "id": 764, - "tag": "Touch" - } - ], - "timestamp_created": "2024-01-12 09:40:48.884748+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/151685", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1256": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1256, - "name": "Stochastic model of the olfactory cilium transduction and adaptation (Antunes et al 2014)", - "repository_type": "github", - "summary": "\" ... In this work, we have combined stochastic computational modeling and\r\n a systematic pharmacological study of different signaling pathways to\r\n investigate their impact during short-term adaptation (STA).\r\n\r\n...\r\nThese results suggest that G-coupled\r\n receptors (GPCRs) cycling is involved with the occurrence of STA. To\r\n gain insights on the dynamical aspects of this process, we developed\r\n a stochastic computational model. The model consists of the olfactory\r\n transduction currents mediated by the cyclic nucleotide gated (CNG)\r\n channels and calcium ion (Ca2+)-activated chloride (CAC) channels,\r\n and the dynamics of their respective ligands, cAMP and Ca2+, and it\r\n simulates the EOG (electroolfactogram)\r\nresults obtained under different experimental\r\n conditions through changes in the amplitude and duration of cAMP and\r\n Ca2+ response, two second messengers implicated with STA\r\n occurrence. The model reproduced the experimental data for each\r\n pharmacological treatment and provided a mechanistic explanation for\r\n the action of GPCR cycling in the levels of second messengers\r\n modulating the levels of STA. All together, these experimental and\r\n theoretical results indicate the existence of a mechanism of\r\n regulation of STA by signaling pathways that control GPCR cycling and\r\n tune the levels of second messengers in OSNs, and not only by CNG\r\n channel desensitization as previously thought. \"", - "tags": [ - { - "id": 1801, - "tag": "COPASI" - }, - { - "id": 572, - "tag": "Calcium dynamics" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 567, - "tag": "Ion Channel Kinetics" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1802, - "tag": "ModelDB:151686" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 751, - "tag": "Signaling pathways" - } - ], - "timestamp_created": "2024-01-12 09:40:49.505114+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/151686", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1257": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1257, - "name": "Statistics of symmetry measure for networks of neurons (Esposito et al. 2014)", - "repository_type": "github", - "summary": "The code reproduces Figures 1, 2, 3A and 3C from Esposito et al \"Measuring symmetry, asymmetry and randomness in neural networks\". It provides the statistics of the symmetry measure defined in the paper for networks of neurons with random connections drawn from uniform and gaussian distributions.", - "tags": [ - { - "id": 855, - "tag": "Connectivity matrix" - }, - { - "id": 655, - "tag": "MATLAB" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1803, - "tag": "ModelDB:151692" - } - ], - "timestamp_created": "2024-01-12 09:40:50.052748+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/151692", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1258": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1258, - "name": "CA1 pyramidal neurons: effect of external electric field from power lines (Cavarretta et al. 2014)", - "repository_type": "github", - "summary": "The paper discusses the effects induced by an electric field at power lines frequency.", - "tags": [ - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 579, - "tag": "Active Dendrites" - }, - { - "id": 578, - "tag": "Activity Patterns" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 571, - "tag": "Detailed Neuronal Models" - }, - { - "id": 734, - "tag": "Extracellular Fields" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 594, - "tag": "I h" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1804, - "tag": "ModelDB:151731" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 596, - "tag": "Synaptic Integration" - } - ], - "timestamp_created": "2024-01-12 09:40:50.632404+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/151731", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1259": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1259, - "name": "Model of repetitive firing in Grueneberg ganglion olfactory neurons (Liu et al., 2012)", - "repository_type": "github", - "summary": "This model is constructed based on properties of Na+ and K+ currents observed in whole-cell patch clamp recordings of mouse Grueneberg ganglion neurons in acute slices. Two distinct Na+ conductances representing the TTX-sensitive and TTX-resistant currents and one delayed rectifier K+ currrent are included. By modulating the maximal conductances of Na+ currents, one can reproduce the regular, phasic, and sporadic patterns of repetitive firing found in the patch clamp experiments.", - "tags": [ - { - "id": 727, - "tag": "Action Potential Initiation" - }, - { - "id": 736, - "tag": "Action Potentials" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 591, - "tag": "I K,leak" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1805, - "tag": "ModelDB:151817" - }, - { - "id": 577, - "tag": "NEURON" - }, - { - "id": 588, - "tag": "Olfaction" - }, - { - "id": 573, - "tag": "Rebound firing" - }, - { - "id": 1789, - "tag": "Recurrent Discharge" - }, - { - "id": 720, - "tag": "Temporal Pattern Generation" - } - ], - "timestamp_created": "2024-01-12 09:40:51.167085+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/151817", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1260": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1260, - "name": "Ionic mechanisms of dendritic spikes (Almog and Korngreen 2014)", - "repository_type": "github", - "summary": "We used a combined experimental and numerical parameter peeling procedure was implemented to optimize a detailed ionic mechanism for the generation and propagation of dendritic spikes in neocortical L5 pyramidal neurons. \r\n\r\nRun the cc_run.hoc to get a demo for dendritic calcium spike generated by coincidence of a back-propagating AP and distal synaptic input.", - "tags": [ - { - "id": 1684, - "tag": "Ca pump" - }, - { - "id": 565, - "tag": "Dendritic Action Potentials" - }, - { - "id": 590, - "tag": "I A" - }, - { - "id": 731, - "tag": "I CAN" - }, - { - "id": 583, - "tag": "I Calcium" - }, - { - "id": 576, - "tag": "I K" - }, - { - "id": 581, - "tag": "I K,Ca" - }, - { - "id": 574, - "tag": "I Na,t" - }, - { - "id": 584, - "tag": "I Potassium" - }, - { - "id": 582, - "tag": "I Sodium" - }, - { - "id": 564, - "tag": "ModelDB" - }, - { - "id": 1806, - "tag": "ModelDB:151825" - }, - { - "id": 577, - "tag": "NEURON" - } - ], - "timestamp_created": "2024-01-12 09:40:51.684089+00:00", - "timestamp_updated": "---", - "uri": "https://github.com/OpenSourceBrain/151825", - "user": { - "email": "info@opensourcebrain.org", - "first_name": "OSB", - "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", - "last_name": "Admin", - "username": "osbadmin" - }, - "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" - }, - "1261": { - "auto_sync": true, - "content_types": "modeling", - "content_types_list": [ - "modeling" - ], - "default_context": "master", - "id": 1261, - "name": "Electrodiffusive astrocytic and extracellular ion concentration dynamics model (Halnes et al. 2013)", - "repository_type": "github", - "summary": "An electrodiffusive formalism was developed for computing the dynamics of the membrane potential\r\nand ion concentrations in the intra- and extracellular space in a one-dimensional geometry (cable). This (general) formalism was implemented in a model of astrocytes exchanging K+, Na+ and Cl- ions with the extracellular space (ECS). \r\nA limited region (0< x