Parameters estimation for spatio-temporal maximum entropy distributions: application to neural spike trains

TL;DR

This paper introduces a numerical method to estimate parameters of spatio-temporal MaxEnt models from neural spike train data, effectively capturing memory effects in large neural networks.

Contribution

It extends previous MaxEnt parameter estimation methods to include temporal constraints, enabling modeling of neural memory effects in large networks.

Findings

Successfully captures memory effects in neural spike trains

Handles large neural network data efficiently

Extends prior spatial-only MaxEnt models

Abstract

We propose a numerical method to learn Maximum Entropy (MaxEnt) distributions with spatio-temporal constraints from experimental spike trains. This is an extension of two papers [10] and [4] who proposed the estimation of parameters where only spatial constraints were taken into account. The extension we propose allows to properly handle memory effects in spike statistics, for large sized neural networks.