Less is more: wiring-economical modular networks support self-sustained firing-economical neural avalanches for efficient processing

Junhao Liang, Sheng Jun Wang, Changsong Zhou*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The brain network is notably cost-efficient, while the fundamental physical and dynamic mechanisms underlying its economical optimization in network structure and activity have not been determined. In this study, we investigate the intricate cost-efficient interplay between structure and dynamics in biologically plausible spatial modular neuronal network models. We observe that critical avalanche states from excitation-inhibition balance under modular network topology with less wiring cost can also achieve lower costs in firing but with strongly enhanced response sensitivity to stimuli. We derive 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 avalanches 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 into brain-inspired efficient computational designs.

Original languageEnglish
Article numbernwab102
Number of pages13
JournalNational Science Review
Volume9
Issue number3
Early online date10 Jun 2021
DOIs
Publication statusPublished - Mar 2022

Scopus Subject Areas

  • General

User-Defined Keywords

  • cost efficiency
  • critical avalanche
  • mean-field theory
  • modular network
  • neural network

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