Critical Avalanches in Excitation-Inhibition Balanced Networks Reconcile Response Reliability with Sensitivity for Optimal Neural Representation.

Neural criticality has emerged as a unified framework that reconciles diverse multiscale neuronal dynamics such as the irregular firing of individual neurons, sparse synchrony in neuronal populations, and the emergence of scale-free avalanches. However, the functional role of neuronal criticality remains ambiguous. Here, we investigate the neural dynamics and representations in response to external signals in excitation-inhibition balanced networks. We reveal that, in contrast with the case for the traditional critical branching model, the critical state of the balanced network simultaneously achieves maximal response sensitivity, maximal response reliability, and the optimal representation of external signals due to the presence of reliable avalanches induced by external signals. We further demonstrate that heterogeneity in inhibitory connections is a mechanism underlying the reliable critical avalanches and optimal representation. Our study addresses a longstanding challenge concerning the functional significance of neuronal criticality, namely the intricate coexistence of reliability and sensitivity.