Stimulus sensitivity of a spiking neural network model

TL;DR

This paper investigates how a spiking neural network model's sensitivity to stimuli varies with its criticality, showing maximal sensitivity near but below criticality using mean-field analysis of Hawkes processes.

Contribution

It introduces a novel measure of stimulus sensitivity for age-dependent Hawkes process models of neural networks and analyzes its dependence on network criticality.

Findings

Maximal sensitivity occurs in the sub-critical regime.

Sensitivity peaks near criticality for biologically relevant parameters.

Mean-field approximation effectively captures network response to stimuli.

Abstract

Some recent papers relate the criticality of complex systems to their maximal capacity of information processing. In the present paper, we consider high dimensional point processes, known as age-dependent Hawkes processes, which have been used to model spiking neural networks. Using mean-field approximation, the response of the network to a stimulus is computed and we provide a notion of stimulus sensitivity. It appears that the maximal sensitivity is achieved in the sub-critical regime, yet almost critical for a range of biologically relevant parameters.