Robust information propagation through noisy neural circuits

TL;DR

This paper investigates how different neural covariance structures affect the robustness of information propagation through noisy, nonlinear neural circuits, highlighting that optimal information encoding and propagation are not always aligned.

Contribution

It identifies covariance structures that optimize information propagation in noisy neural circuits, revealing that optimal encoding and propagation can differ.

Findings

Covariance structures with differential correlations can reduce encoded information.

Optimal propagation of information may require different covariance patterns than those maximizing encoding.

Certain covariance structures enhance the robustness of information transfer through nonlinear, noisy neural pathways.

Abstract

Sensory neurons give highly variable responses to stimulation, which can limit the amount of stimulus information available to downstream circuits. Much work has investigated the factors that affect the amount of information encoded in these population responses, leading to insights about the role of covariability among neurons, tuning curve shape, etc. However, the informativeness of neural responses is not the only relevant feature of population codes; of potentially equal importance is how robustly that information propagates to downstream structures. For instance, to quantify the retina's performance, one must consider not only the informativeness of the optic nerve responses, but also the amount of information that survives the spike-generating nonlinearity and noise corruption in the next stage of processing, the lateral geniculate nucleus. Our study identifies the set of covariance structures for the upstream cells that optimize the ability of information to propagate through noisy, nonlinear circuits. Within this optimal family are covariances with "differential correlations", which are known to reduce the information encoded in neural population activities. Thus, covariance structures that maximize information in neural population codes, and those that maximize the ability of this information to propagate, can be very different.