Less is More: Wiring-Economical Modular Networks Support Self-Sustained Firing-Economical Neural Avalanches for Efficient Processing

TL;DR

This paper investigates how modular neural networks can achieve cost-efficient, critical avalanche dynamics with low wiring and firing costs, enhancing response sensitivity, and explains the underlying mechanisms through mean-field theory.

Contribution

It introduces a novel approximate theory linking network structure and dynamics, revealing how modular topology supports economical and critical neural activity.

Findings

Critical avalanche states are achievable with less wiring cost.

Low firing cost and enhanced response are linked to proximity to a Hopf bifurcation.

The study provides a generic mechanism for cost-efficient modular neural organization.

Abstract

Brain network is remarkably cost-efficient while the fundamental physical and dynamical mechanisms underlying its economical optimization in network structure and activity are not clear. Here we study intricate cost-efficient interplay between structure and dynamics in biologically plausible spatial modular neuronal network models. We find that critical avalanche states from excitation-inhibition balance, under modular network topology with less wiring cost, can also achieve less costs in firing, but with strongly enhanced response sensitivity to stimuli. We derived mean-field equations that govern the macroscopic network dynamics through a novel approximate theory. The mechanism of low firing cost and stronger response in the form of critical avalanche is explained as a proximity to a Hopf bifurcation of the modules when increasing their connection density. Our work reveals the generic mechanism underlying the cost-efficient modular organization and critical dynamics widely observed in neural systems, providing insights to brain-inspired efficient computational designs.