A small-correlation expansion to quantify information in noisy sensory systems

TL;DR

This paper introduces a small-correlation expansion method to analytically quantify the information transmitted by large neural populations, accounting for noise and correlations, validated on synthetic and real retinal data.

Contribution

The paper presents a novel analytical approach to estimate information in large neural populations considering correlations, overcoming previous computational limitations.

Findings

The expansion accurately estimates information in synthetic data.

It reveals the impact of noise correlations on information transmission.

Applied to retinal data, it quantifies the role of correlations and memory in neural coding.

Abstract

Neural networks encode information through their collective spiking activity in response to external stimuli. This population response is noisy and strongly correlated, with complex interplay between correlations induced by the stimulus, and correlations caused by shared noise. Understanding how these correlations affect information transmission has so far been limited to pairs or small groups of neurons, because the curse of dimensionality impedes the evaluation of mutual information in larger populations. Here we develop a small-correlation expansion to compute the stimulus information carried by a large population of neurons, yielding interpretable analytical expressions in terms of the neurons' firing rates and pairwise correlations. We validate the approximation on synthetic data and demonstrate its applicability to electrophysiological recordings in the vertebrate retina, allowing us to quantify the effects of noise correlations between neurons and of memory in single neurons.