1. Электронный научный архив УрФУ
  2. 00. Общеуниверситетские коллекции
  3. Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC
Пожалуйста, используйте этот идентификатор, чтобы цитировать или ссылаться на этот ресурс: http://elar.urfu.ru/handle/10995/130611
Полная запись метаданных
Поле DCЗначениеЯзык
dc.contributor.authorBashkirtseva, I.en
dc.contributor.authorPisarchik, A. N.en
dc.contributor.authorRyashko, L.en
dc.date.accessioned2024-04-05T16:27:17Z-
dc.date.available2024-04-05T16:27:17Z-
dc.date.issued2023-
dc.identifier.citationBashkirtseva, I, Pisarchik, AN & Ryashko, L 2023, 'Multistability and stochastic dynamics of Rulkov neurons coupled via a chemical synapse', Communications in Nonlinear Science and Numerical Simulation, Том. 125, 107383. https://doi.org/10.1016/j.cnsns.2023.107383harvard_pure
dc.identifier.citationBashkirtseva, I., Pisarchik, A. N., & Ryashko, L. (2023). Multistability and stochastic dynamics of Rulkov neurons coupled via a chemical synapse. Communications in Nonlinear Science and Numerical Simulation, 125, [107383]. https://doi.org/10.1016/j.cnsns.2023.107383apa_pure
dc.identifier.issn1007-5704-
dc.identifier.otherFinal2
dc.identifier.otherAll Open Access, Hybrid Gold3
dc.identifier.otherhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85163986624&doi=10.1016%2fj.cnsns.2023.107383&partnerID=40&md5=124dd290ecbbb134d6e714232e02c7441
dc.identifier.otherhttps://doi.org/10.1016/j.cnsns.2023.107383pdf
dc.identifier.urihttp://elar.urfu.ru/handle/10995/130611-
dc.description.abstractWe study complex dynamics of two Rulkov neurons unidirectionally connected via a chemical synapse with respect to three control parameters: (i) a parameter responsible for the type of dynamical behavior of a solitary neuron, (ii) coupling strength, and (iii) noise intensity. The coupled system exhibits various scenarios on the route from a stable equilibrium to chaos with respect to the coupling strength. We observe a variety of dynamical regimes, including mono-, bi- and tri-stability, order-chaos transitions and vice versa, as well as the coexistence of in-phase and anti-phase synchronization. We also study transitions between in-phase and out-of-phase synchronization with statistics on the duration of synchronization intervals and transitions from order to chaos. In addition to numerical simulations, we demonstrate the effectiveness of the analytical confidence ellipses method based on stochastic sensitivity approach. © 2023 The Author(s)en
dc.description.sponsorshipRussian Science Foundation, RSF: 21-11-00062en
dc.description.sponsorshipThe work was supported by the Russian Science Foundation (project No. 21-11-00062).en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherElsevier B.V.en
dc.relationinfo:eu-repo/grantAgreement/RSF//21-11-00062en
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.rightscc-by-nc-ndother
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/unpaywall
dc.sourceCommunications in Nonlinear Science and Numerical Simulation2
dc.sourceCommunications in Nonlinear Science and Numerical Simulationen
dc.subjectCHAOSen
dc.subjectCHEMICAL SYNAPSEen
dc.subjectMAP-BASED NEURON MODELSen
dc.subjectNEURONAL DYNAMICSen
dc.subjectNOISE-INDUCED EFFECTSen
dc.subjectSTOCHASTIC SENSITIVITYen
dc.subjectSYNCHRONIZATIONen
dc.subjectDYNAMICSen
dc.subjectNEURONSen
dc.subjectNUMERICAL METHODSen
dc.subjectSTOCHASTIC MODELSen
dc.subjectSTOCHASTIC SYSTEMSen
dc.subjectCHEMICAL SYNAPSEen
dc.subjectCOUPLING STRENGTHSen
dc.subjectIN-PHASEen
dc.subjectMAP-BASED NEURON MODELen
dc.subjectMULTISTABILITYen
dc.subjectNEURON MODELINGen
dc.subjectNEURONAL DYNAMICSen
dc.subjectNOISE-INDUCED EFFECTen
dc.subjectSTOCHASTIC SENSITIVITYen
dc.subjectSTOCHASTICSen
dc.subjectSYNCHRONIZATIONen
dc.titleMultistability and stochastic dynamics of Rulkov neurons coupled via a chemical synapseen
dc.typeArticleen
dc.typeinfo:eu-repo/semantics/articleen
dc.type|info:eu-repo/semantics/publishedVersionen
dc.identifier.doi10.1016/j.cnsns.2023.107383-
dc.identifier.scopus85163986624-
local.contributor.employeeBashkirtseva, I., Institute of Natural Sciences and Mathematics, Ural Federal University, Lenina, 51, Ekaterinburg, 620000, Russian Federationen
local.contributor.employeePisarchik, A.N., Center for Biomedical Technology, Universidad Politécnica de Madrid, Campus Montegancedo, 28223, Pozuelo de Alarcón, Madrid, Spainen
local.contributor.employeeRyashko, L., Institute of Natural Sciences and Mathematics, Ural Federal University, Lenina, 51, Ekaterinburg, 620000, Russian Federationen
local.volume125-
dc.identifier.wos001029016100001-
local.contributor.departmentInstitute of Natural Sciences and Mathematics, Ural Federal University, Lenina, 51, Ekaterinburg, 620000, Russian Federationen
local.contributor.departmentCenter for Biomedical Technology, Universidad Politécnica de Madrid, Campus Montegancedo, 28223, Pozuelo de Alarcón, Madrid, Spainen
local.identifier.pure41586122-
local.description.order107383-
local.identifier.eid2-s2.0-85163986624-
local.fund.rsf21-11-00062-
local.identifier.wosWOS:001029016100001-
Располагается в коллекциях:Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC

Файлы этого ресурса:
Файл Описание РазмерФормат 
2-s2.0-85163986624.pdf2,5 MBAdobe PDFПросмотреть/Открыть
Показать базовое описание ресурса Статистика  Google Scholar





Лицензия на ресурс: Лицензия Creative Commons Creative Commons