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2020-11-30Zeitschriftenartikel DOI: 10.18452/24970
Influence of Autapses on Synchronization in Neural Networks With Chemical Synapses
dc.contributor.authorProtachevicz, Paulo R.
dc.contributor.authorIarosz, Kelly C.
dc.contributor.authorCaldas, Iberê L.
dc.contributor.authorAntonopoulos, Chris G.
dc.contributor.authorBatista, Antonio M.
dc.contributor.authorKurths, Jurgen
dc.date.accessioned2022-07-07T11:10:54Z
dc.date.available2022-07-07T11:10:54Z
dc.date.issued2020-11-30none
dc.date.updated2020-12-14T06:28:46Z
dc.identifier.urihttp://edoc.hu-berlin.de/18452/25657
dc.description.abstractA great deal of research has been devoted on the investigation of neural dynamics in various network topologies. However, only a few studies have focused on the influence of autapses, synapses from a neuron onto itself via closed loops, on neural synchronization. Here, we build a random network with adaptive exponential integrate-and-fire neurons coupled with chemical synapses, equipped with autapses, to study the effect of the latter on synchronous behavior. We consider time delay in the conductance of the pre-synaptic neuron for excitatory and inhibitory connections. Interestingly, in neural networks consisting of both excitatory and inhibitory neurons, we uncover that synchronous behavior depends on their synapse type. Our results provide evidence on the synchronous and desynchronous activities that emerge in random neural networks with chemical, inhibitory and excitatory synapses where neurons are equipped with autapses.eng
dc.language.isoengnone
dc.publisherHumboldt-Universität zu Berlin
dc.rights(CC BY 4.0) Attribution 4.0 Internationalger
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectsynchronizationeng
dc.subjectneural dynamicseng
dc.subjectintegrate-and-fire modeleng
dc.subjectexcitatory and inhibitory neural networkseng
dc.subjectsynapseseng
dc.subjectautapseseng
dc.subject.ddc500 Naturwissenschaften und Mathematiknone
dc.titleInfluence of Autapses on Synchronization in Neural Networks With Chemical Synapsesnone
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/25657-1
dc.identifier.doihttp://dx.doi.org/10.18452/24970
dc.type.versionpublishedVersionnone
local.edoc.pages13none
local.edoc.type-nameZeitschriftenartikel
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
dc.description.versionPeer Reviewednone
dc.identifier.eissn1662-5137
dcterms.bibliographicCitation.doi10.3389/fnsys.2020.604563none
dcterms.bibliographicCitation.journaltitleFrontiers in systems neurosciencenone
dcterms.bibliographicCitation.volume14none
dcterms.bibliographicCitation.articlenumber604563none
dcterms.bibliographicCitation.originalpublishernameFrontiers Research Foundationnone
dcterms.bibliographicCitation.originalpublisherplaceLausannenone
bua.departmentMathematisch-Naturwissenschaftliche Fakultätnone

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