# A joint maximum-entropy model for binary neural population patterns and continuous signals

## Files

**MaxEntFit.zip**(11 KB)

## Description

This is a collection of python functions to reproduce experiments/figures from the following paper:```
@inproceedings{Gerwinn2009a,
author = "S. Gerwinn and P. Berens and M. Bethge",
title = "A joint maximum-entropy model for binary neural population patterns and continuous signals",
year = 2009,
booktitle = "Advances in Neural Information Processing Systems 22",
keywords = "maximum entropy, population coding"
}
```

*maxentfit.py:*

Contains the algorithm for fitting a maximum entropy model to observed moments. This is done by gradient ascent on the log-likelihood. The moments needed for the gradient are computed by brute force enumeration of all (binary) states.

*example.py:*

Contains a one-dimensional example and should illustrate how to use the code in maxentfit. It produces figure 1 of the above mentioned publication.

*minimize_carl.py:*

Conjugate gradient minimizer by Carl Rasmussen. Can probably be replaced by any other gradient based optimizer.

## References

S. Gerwinn,
P. Berens, and
M. Bethge

#maximum entropy, #population coding

Code, PDF, BibTex

**A joint maximum-entropy model for binary neural population patterns and continuous signals***Advances in Neural Information Processing Systems 22, 2009*#maximum entropy, #population coding

Code, PDF, BibTex