Excitation-inhibition balance controls information encoding in neural populations

TL;DR

This paper investigates how the balance of excitation and inhibition in neural populations influences information encoding, revealing that optimal information processing occurs at the edge of stability and involves a trade-off between sensitivity and accuracy.

Contribution

It demonstrates that excitation-inhibition balance maximizes information encoding at the edge of stability and elucidates the trade-off between short-term sensitivity and long-term accuracy.

Findings

Maximum information at the edge of stability where excitation balances inhibition.

Stronger inhibition enhances instantaneous sensitivity during prolonged stimuli.

Balance controls optimal information-processing in neural populations.

Abstract

Understanding how the complex connectivity structure of the brain shapes its information-processing capabilities is a long-standing question. By focusing on a paradigmatic architecture, we study how the neural activity of excitatory and inhibitory populations encodes information on external signals. We show that at long times information is maximized at the edge of stability, where inhibition balances excitation, both in linear and nonlinear regimes. In the presence of multiple external signals, this maximum corresponds to the entropy of the input dynamics. By analyzing the case of a prolonged stimulus, we find that stronger inhibition is instead needed to maximize the instantaneous sensitivity, revealing an intrinsic trade-off between short-time responses and long-time accuracy. In agreement with recent experimental findings, our results pave the way for a deeper information-theoretic understanding of how the balance between excitation and inhibitions controls optimal information-processing in neural populations.