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+This code implements the models from:
+
+Goodman DFM, Brette R, 2010 Spike-Timing-Based Computation in Sound Localization. *PLoS Comput Biol* 6(11): e1000993.  
+doi:10.1371/journal.pcbi.1000993
+
+Code is not provided to generate all the data and the figures, but the three basic models (approximate/ideal/allpairs) are shown.
+
+## Installation
+
+You will need to download and extract the IRCAM LISTEN database:
+
+http://recherche.ircam.fr/equipes/salles/listen/
+
+The files should be extracted to a folder something like:
+
+```
+F:\HRTF\IRCAM\IRC_1002
+etc.
+```
+
+You will need to change the file `shared.py` to give the location of this database (see below).
+
+In addition, you will need a copy of Python 2.5, 2.6 or 2.7 and the packages numpy, scipy, matplotlib. You will also need the Brian neural network simulator package, version 1.3 or above:
+
+http://www.briansimulator.org/
+
+## Guide to files
+
+### approximate_filtering_model.py  
+### ideal_filtering_model.py
+
+The approximate and ideal filtering models.
+
+### all_pairs_model.py
+
+The learning model. The implementation of the model is included, but not code for generating the learned ITD/ILD pairs: this code is mostly just technical file management stuff, so it is not included for simplicity.
+
+### hrtf_analysis.py
+
+Generate best gain/delay pairs for the approximate filtering model, and find the normalisation factors for the ideal filtering model. Results are saved so only need to be generated once.
+
+### models.py
+
+The neural models used. Changing these equations and parameters can be used to easily switch to different models. Only the leaky integrate-and-fire model is given.
+
+### plot_count.py
+
+A function for plotting the outputs of the approximate/ideal filtering model, specialised for the IRCAM LISTEN database.
+
+### shared.py
+
+Various imports and variables that are shared across all of the models. You should change the `ircam_locations` variable in the `get_ircam()` function to reflect the location where you have saved the IRCAM data.
+
+---
+
+2025-06-02: Converted README to Markdown.
\ No newline at end of file
diff --git a/readme.txt b/readme.txt
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-This code implements the models from:
-
-	Goodman DFM, Brette R, 2010 Spike-Timing-Based Computation in Sound
-	Localization. PLoS Comput Biol 6(11): e1000993.
-	doi:10.1371/journal.pcbi.1000993
-
-Code is not provided to generate all the data and the figures, but the three
-basic models (approximate/ideal/allpairs) are shown.
-
-Installation
-------------
-
-You will need to download and extract the IRCAM LISTEN database:
-
-	http://recherche.ircam.fr/equipes/salles/listen/
-	
-The files should be extracted to a folder something like:
-
-	F:\HRTF\IRCAM\IRC_1002
-	etc.
-	
-You will need to change the file shared.py to give the location of this database
-(see below).
-
-In addition, you will need a copy of Python 2.5, 2.6 or 2.7 and the packages
-numpy, scipy, matplotlib. You will also need the Brian neural network simulator
-package, version 1.3 or above:
-
-	http://www.briansimulator.org/
-
-Guide to files
---------------
-	
-approximate_filtering_model.py
-ideal_filtering_model.py
-
-	The approximate and ideal filtering models.
-	
-all_pairs_model.py
-
-	The learning model. The implementation of the model is included, but not
-	code for generating the learned ITD/ILD pairs: this code is mostly just
-        technical file management stuff, so it is not included for simplicity.
-	
-hrtf_analysis.py
-
-	Generate best gain/delay pairs for the approximate filtering model, and
-	find the normalisation factors for the ideal filtering model. Results are
-	saved so only need to be generated once.
-	
-models.py
-
-	The neural models used. Changing these equations and parameters can be used
-	to easily switch to different models. Only the leaky integrate-and-fire
-	model is given.
-	
-plot_count.py
-
-	A function for plotting the outputs of the approximate/ideal filtering
-	model, specialised for the IRCAM LISTEN database.
-	
-shared.py
-
-	Various imports and variables that are shared across all of the models.
-	You should change the ircam_locations variable in the get_ircam() function
-	to reflect the location where you have saved the IRCAM data.