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+# README for Brian Code Associated with the Paper
+
+Peter beim Graben and Serafim Rodrigues
+
+A Biophysical observation model for field potentials of networks of  
+leaky-integrate-and-fire neurons.  
+*Front. Comput. Neurosci*, 04 January 2013  
+doi: 10.3389/fncom.2012.00100
+
+*******************************
+
+This python code, Gunft6.py, requires and runs under the Brian simulator.
+
+## Note for developers:
+
+- **Note 1:** As it stands, the code is not efficient (fast) as it does not use the facilities vector processing and uses a lot of for-loops which is not efficient. So it can be improved.  
+- **Note 2:** Periodic thalamic input is not yet implemented.
+
+*****************************
+
+1. This is a network of 5000 neurons, 80% of which excitatory, and 20% inhibitory.  
+2. The network is randomly connected (between pairs) with connection probability = 0.2.  
+3. Both Excitatory and Inhibitory neurons are described via LIF model.  
+4. The currents are double exponential, but the excitatory currents can receive external noise.
+
+---
+
+2025-07-09: Converted README to Markdown.
\ No newline at end of file
diff --git a/readme.txt b/readme.txt
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-This is the readme for the Brian code associated with the paper:
-
-Peter beim Graben and Serafim Rodrigues
-
-A Biophysical observation model for field potentials of networks of
-leaky-integrate-and-fire neurons.
-Front. Comput. Neurosci, 04 January 2013
-doi: 10.3389/fncom.2012.00100
-
-*******************************
-This python code, Gunft6.py, requires and runs under the Brian
-simulator.
-
-Note for developers:
-
-Note 1 : As it stands, the code is not effiecient (fast) as it does
-not use the facilties vector processing and uses a lot of for-loops
-which is not efficient. So it can be improved.
-Note 2: Periodic thalamic input is not yet implemented.
-
-*****************************
-
-1) This is a network of 5000 neurons, 80% of which excitatory, and 20%
-   inhibitory.
-2) The network is randomy connected (between pairs) with connection
-   probability = 0.2.
-3) Both Excitatory and Inhibitory neurons are described via LIF model.
-4) The currents are double exponetial, but the excitatory currents can
-   recieve external noise.