Information capacity and transmission are maximized in balanced cortical networks with neuronal avalanches

TL;DR

This study demonstrates that cortical networks with balanced excitation and inhibition, exhibiting neuronal avalanches, maximize their information capacity and transmission, highlighting the importance of criticality in neural processing across various species.

Contribution

It reveals that optimal information processing occurs at a specific E/I balance where neuronal avalanches emerge, supported by experimental and modeling evidence.

Findings

Maximum information capacity at intermediate E/I balance

Neuronal avalanches linked to peak information transmission

Model predictions align with in vivo measurements

Abstract

The repertoire of neural activity patterns that a cortical network can produce constrains the network's ability to transfer and process information. Here, we measured activity patterns obtained from multi-site local field potential (LFP) recordings in cortex cultures, urethane anesthetized rats, and awake macaque monkeys. First, we quantified the information capacity of the pattern repertoire of ongoing and stimulus-evoked activity using Shannon entropy. Next, we quantified the efficacy of information transmission between stimulus and response using mutual information. By systematically changing the ratio of excitation/inhibition (E/I) in vitro and in a network model, we discovered that both information capacity and information transmission are maximized at a particular intermediate E/I, at which ongoing activity emerges as neuronal avalanches. Next, we used our in vitro and model results to correctly predict in vivo information capacity and interactions between neuronal groups during ongoing activity. Close agreement between our experiments and model suggest that neuronal avalanches and peak information capacity arise due to criticality and are general properties of cortical networks with balanced E/I.