Prediction of spatio-temporal patterns of neural activity from pairwise correlations

TL;DR

This paper introduces a maximum entropy-based model that predicts neural population spatio-temporal patterns more accurately by incorporating both spatial and temporal pairwise correlations, outperforming traditional models.

Contribution

The authors develop a novel Markovian maximum entropy model that captures both spatial and temporal correlations in neural activity, improving pattern prediction accuracy.

Findings

Model accurately predicts spatio-temporal pattern probabilities.

Outperforms Ising models with only pairwise correlations.

Effective on both simulated and experimental neural data.

Abstract

We designed a model-based analysis to predict the occurrence of population patterns in distributed spiking activity. Using a maximum entropy principle with a Markovian assumption, we obtain a model that accounts for both spatial and temporal pairwise correlations among neurons. This model is tested on data generated with a Glauber spin-glass system and is shown to correctly predict the occurrence probabilities of spatio-temporal patterns significantly better than Ising models taking into account only pairwise correlations. This increase of predictability was also observed on experimental data recorded in parietal cortex during slow-wave sleep. This approach can also be used to generate surrogates that reproduce the spatial and temporal correlations of a given data set.