Topological determinants of self- sustained activity in a simple model of excitable dynamics on graphs - Sorbonne Université
Journal Articles Scientific Reports Year : 2017

Topological determinants of self- sustained activity in a simple model of excitable dynamics on graphs

Abstract

Simple models of excitable dynamics on graphs are an efficient framework for studying the interplay between network topology and dynamics. This topic is of practical relevance to diverse fields, ranging from neuroscience to engineering. Here we analyze how a single excitation propagates through a random network as a function of the excitation threshold, that is, the relative amount of activity in the neighborhood required for the excitation of a node. We observe that two sharp transitions delineate a region of sustained activity. Using analytical considerations and numerical simulation, we show that these transitions originate from the presence of barriers to propagation and the excitation of topological cycles, respectively, and can be predicted from the network topology. Our findings are interpreted in the context of network reverberations and self-sustained activity in neural systems, which is a question of long-standing interest in computational neuroscience.
Fichier principal
Vignette du fichier
srep42340.pdf (1.01 Mo) Télécharger le fichier
Origin Publication funded by an institution

Dates and versions

hal-01487876 , version 1 (13-03-2017)

Licence

Identifiers

Cite

Christoph C Fretter, Annick C Lesne, Claus C. Hilgetag, Marc-Thorsten C Hütt. Topological determinants of self- sustained activity in a simple model of excitable dynamics on graphs. Scientific Reports, 2017, 7, pp.42340. ⟨10.1038/srep42340⟩. ⟨hal-01487876⟩
308 View
180 Download

Altmetric

Share

More