Clustering promotes switching dynamics in networks of noisy neurons

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dc.citation.issue2
dc.citation.rankM21a
dc.citation.spage023111
dc.citation.volume28
dc.contributor.authorFranović, Igor
dc.contributor.authorKlinshov, Vladimir
dc.date.accessioned2024-07-03T09:31:22Z
dc.date.available2024-07-03T09:31:22Z
dc.date.issued2018-02-21
dc.description.abstractMacroscopic variability is an emergent property of neural networks, typically manifested in spontaneous switching between the episodes of elevated neuronal activity and the quiescent episodes. We investigate the conditions that facilitate switching dynamics, focusing on the interplay between the different sources of noise and heterogeneity of the network topology. We consider clustered networks of rate-based neurons subjected to external and intrinsic noise and derive an effective model where the network dynamics is described by a set of coupled second-order stochastic mean-field systems representing each of the clusters. The model provides an insight into the different contributions to effective macroscopic noise and qualitatively indicates the parameter domains where switching dynamics may occur. By analyzing the mean-field model in the thermodynamic limit, we demonstrate that clustering promotes multistability, which gives rise to switching dynamics in a considerably wider parameter region compared to the case of a non-clustered network with sparse random connection topology.
dc.identifier.doi10.1063/1.5017822
dc.identifier.issn1054-1500
dc.identifier.issn1089-7682
dc.identifier.scopus2-s2.0-85042596611
dc.identifier.urihttps://pub.ipb.ac.rs/handle/123456789/175
dc.identifier.wos000427002200012
dc.language.isoen
dc.publisherAmerican Institute of Physics Inc.
dc.relation.ispartofChaos: An Interdisciplinary Journal of Nonlinear Science
dc.relation.ispartofabbrChaos
dc.rightsrestrictedAccess
dc.titleClustering promotes switching dynamics in networks of noisy neurons
dc.typeArticle
dc.type.versionpublishedVersion
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