The sign rule and beyond: Boundary effects, flexibility, and noise correlations in neural population codes

TL;DR

This paper investigates how noise correlations in neural populations affect stimulus encoding, proving a sign rule for improved coding, exploring boundary effects of optimal correlations, and establishing conditions for noise-free performance.

Contribution

It generalizes the sign rule for noise correlations, characterizes boundary conditions for optimal correlations, and provides criteria for noise-free coding in neural populations.

Findings

Sign rule improves coding with opposite signs of noise and signal correlations

Optimal correlation structures lie on the boundaries of possible noise correlations

Conditions identified where noise does not impair coding performance

Abstract

Over repeat presentations of the same stimulus, sensory neurons show variable responses. This "noise" is typically correlated between pairs of cells, and a question with rich history in neuroscience is how these noise correlations impact the population's ability to encode the stimulus. Here, we consider a very general setting for population coding, investigating how information varies as a function of noise correlations, with all other aspects of the problem - neural tuning curves, etc. - held fixed. This work yields unifying insights into the role of noise correlations. These are summarized in the form of theorems, and illustrated with numerical examples involving neurons with diverse tuning curves. Our main contributions are as follows. (1) We generalize previous results to prove a sign rule (SR) - if noise correlations between pairs of neurons have opposite signs vs. their signal correlations, then coding performance will improve compared to the independent case. This holds for three different metrics of coding performance, and for arbitrary tuning curves and levels of heterogeneity. This generality is true for our other results as well. (2) As also pointed out in the literature, the SR does not provide a necessary condition for good coding. We show that a diverse set of correlation structures can improve coding. Many of these violate the SR, as do experimentally observed correlations. There is structure to this diversity: we prove that the optimal correlation structures must lie on boundaries of the possible set of noise correlations. (3) We provide a novel set of necessary and sufficient conditions, under which the coding performance (in the presence of noise) will be as good as it would be if there were no noise present at all.