High-Fidelity Coding with Correlated Neurons

TL;DR

This paper demonstrates that positive correlations among neurons can dramatically improve coding accuracy and capacity, challenging previous beliefs that such correlations are mostly detrimental.

Contribution

It reveals that specific correlation patterns can greatly enhance neural coding performance, even with realistic correlation levels and small neuron populations.

Findings

Error probability can be reduced by many orders of magnitude.

Neural capacity can be increased by large factors.

Correlation patterns can lead to near-perfect stimulus discrimination.

Abstract

Positive correlations in the activity of neurons are widely observed in the brain. Previous studies have shown these correlations to be detrimental to the fidelity of population codes or at best marginally favorable compared to independent codes. Here, we show that positive correlations can enhance coding performance by astronomical factors. Specifically, the probability of discrimination error can be suppressed by many orders of magnitude. Likewise, the number of stimuli encoded--the capacity--can be enhanced by similarly large factors. These effects do not necessitate unrealistic correlation values and can occur for populations with a few tens of neurons. We further show that both effects benefit from heterogeneity commonly seen in population activity. Error suppression and capacity enhancement rest upon a pattern of correlation. In the limit of perfect coding, this pattern leads to a `lock-in' of response probabilities that eliminates variability in the subspace relevant for stimulus discrimination. We discuss the nature of this pattern and suggest experimental tests to identify it.