Rate chaos and memory lifetime in spiking neural networks

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dc.citation.rankM21a
dc.citation.spage112011
dc.citation.volume158
dc.contributor.authorKlinshov, Vladimir V.
dc.contributor.authorKovalchuk, Andrey V.
dc.contributor.authorFranović, Igor
dc.contributor.authorPerc, Matjaž
dc.contributor.authorSvetec, Milan
dc.date.accessioned2023-05-25T09:16:59Z
dc.date.available2023-05-25T09:16:59Z
dc.date.issued2022-05
dc.description.abstractRate chaos is a collective state of a neural network characterized by slow irregular fluctuations of firing rates of individual neurons. We study a sparsely connected network of spiking neurons which demonstrates three different scenarios for the emergence of rate chaos, based either on increasing the synaptic strength, increasing the synaptic integration time, or clustering of the excitatory synaptic connections. Although all the scenarios lead to collective dynamics with similar statistical features, it turns out that the implications for the computational capability of the network in performing a simple delay task are strongly dependent on the particular scenario. Namely, only the scenario involving slow dynamics of synapses results in an appreciable extension of the network's dynamic memory. In other cases, the dynamic memory remains short despite the emergence of long timescales in the neuronal spike trains. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).
dc.identifier.doi10.1016/j.chaos.2022.112011
dc.identifier.issn0960-0779
dc.identifier.scopus2-s2.0-85126928724
dc.identifier.urihttps://pub.ipb.ac.rs/handle/123456789/38
dc.identifier.wos000798694800007
dc.language.isoen
dc.publisherElsevier BV
dc.relation.ispartofChaos, Solitons and Fractals
dc.relation.ispartofabbrChaos, Solitons and Fractals
dc.rightsopenAccess
dc.titleRate chaos and memory lifetime in spiking neural networks
dc.typejournal-article
dc.type.versionpublishedVersion
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