Spatio-temporal spike trains analysis for large scale networks using maximum entropy principle and Monte-Carlo method

TL;DR

This paper reviews recent advances in spike train analysis using maximum entropy models and introduces a new Monte-Carlo sampling method for fitting large-scale spatio-temporal neural data models.

Contribution

It extends maximum entropy models to include temporal dynamics and proposes a Monte-Carlo method for efficient fitting of large neural datasets.

Findings

Generalized MaxEnt models to include temporal correlations

Developed a Monte-Carlo sampling method for large-scale data

Facilitated fitting of complex neural activity models

Abstract

Understanding the dynamics of neural networks is a major challenge in experimental neuroscience. For that purpose, a modelling of the recorded activity that reproduces the main statistics of the data is required. In a first part, we present a review on recent results dealing with spike train statistics analysis using maximum entropy models (MaxEnt). Most of these studies have been focusing on modelling synchronous spike patterns, leaving aside the temporal dynamics of the neural activity. However, the maximum entropy principle can be generalized to the temporal case, leading to Markovian models where memory effects and time correlations in the dynamics are properly taken into account. In a second part, we present a new method based on Monte-Carlo sampling which is suited for the fitting of large-scale spatio-temporal MaxEnt models. The formalism and the tools presented here will be essential to fit MaxEnt spatio-temporal models to large neural ensembles.