An analytically tractable model of neural population activity in the presence of common input explains higher-order correlations and entropy

TL;DR

This paper presents an analytically solvable model demonstrating how common input correlations influence higher-order neural correlations and entropy, explaining recent experimental observations.

Contribution

It introduces a simple, analytically tractable model linking common input correlations to higher-order neural activity patterns and entropy measures.

Findings

Small changes in second-order correlations cause large shifts in higher-order correlations.

Higher-order correlations significantly affect entropy, sparsity, and statistical heat capacity.

Model explains recent experimental observations of neural population activity.

Abstract

Simultaneously recorded neurons exhibit correlations whose underlying causes are not known. Here, we use a population of threshold neurons receiving correlated inputs to model neural population recordings. We show analytically that small changes in second-order correlations can lead to large changes in higher correlations, and that these higher-order correlations have a strong impact on the entropy, sparsity and statistical heat capacity of the population. Remarkably, our findings for this simple model may explain a couple of surprising effects recently observed in neural population recordings.