Communities in Neuronal Complex Networks Revealed by Activation Patterns

TL;DR

This paper demonstrates that incorporating exponential decay of activation in neuronal networks improves community detection during initial transient dynamics, across various network models.

Contribution

It introduces a method that uses activation decay patterns to better identify communities in neuronal networks, extending previous approaches without decay considerations.

Findings

Activation decay enhances community separation.

Moderate decay levels optimize discrimination.

Method applies to diverse network models.

Abstract

Recently, it has been shown that the communities in neuronal networks of the integrate-and-fire type can be identified by considering patterns containing the beginning times for each cell to receive the first non-zero activation. The received activity was integrated in order to facilitate the spiking of each neuron and to constrain the activation inside the communities, but no time decay of such activation was considered. The present article shows that, by taking into account exponential decays of the stored activation, it is possible to identify the communities also in terms of the patterns of activation along the initial steps of the transient dynamics. The potential of this method is illustrated with respect to complex neuronal networks involving four communities, each of a different type (Erd\H{o}s-R\'eny, Barab\'asi-Albert, Watts-Strogatz as well as a simple geographical model). Though the consideration of activation decay has been found to enhance the communities separation, too intense decays tend to yield less discrimination.