Impact of triplet correlations on neural population codes

TL;DR

This paper investigates how higher-order triplet correlations in neural populations influence stimulus encoding, revealing that stimulus-dependent triplet correlations can significantly enhance coding accuracy in small neural ensembles.

Contribution

It demonstrates that triplet correlations, when stimulus-dependent, can improve neural coding by increasing the distinguishability of stimuli, a novel insight into higher-order neural interactions.

Findings

Triplet correlations can skew response distributions to improve stimulus discrimination.

Stimulus-dependent triplet correlations enhance coding performance.

Estimations of recording times needed to measure triplet correlations accurately.

Abstract

Which statistical features of spiking activity matter for how stimuli are encoded in neural populations? A vast body of work has explored how firing rates in individual cells and correlations in the spikes of cell pairs impact coding. But little is known about how higher-order correlations, which describe simultaneous firing in triplets and larger ensembles of cells, impact encoded stimulus information. Here, we take a first step toward closing this gap. We vary triplet correlations in small (~10 cell) neural populations while keeping single cell and pairwise statistics fixed at typically reported values. For each value of triplet correlations, we estimate the performance of the neural population on a two-stimulus discrimination task. We identify a predominant way that such triplet correlations can strongly enhance coding: if triplet correlations differ for the two stimuli, they skew the response distributions of the two stimuli apart from each other, separating them and making them easier to distinguish. This coding benefit does not occur when both stimuli elicit similar triplet correlations. These results indicate that higher-order correlations could have a strong effect on population coding. Finally, we calculate how many samples are necessary to accurately measure spiking correlations of this type, providing an estimate of the necessary recording times in experiments.