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Multistability and stochastic dynamics of Rulkov neurons coupled via a chemical synapse. / Bashkirtseva, Irina; Pisarchik, Alexander N.; Ryashko, Lev.
In: Communications in Nonlinear Science and Numerical Simulation, Vol. 125, 107383, 01.10.2023.

Research output: Contribution to journal › Article › peer-review

Harvard

Bashkirtseva, 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, vol. 125, 107383. https://doi.org/10.1016/j.cnsns.2023.107383

APA

Bashkirtseva, 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.2023.107383

Vancouver

Bashkirtseva I, Pisarchik AN, Ryashko L. Multistability and stochastic dynamics of Rulkov neurons coupled via a chemical synapse. Communications in Nonlinear Science and Numerical Simulation. 2023 Oct 1;125:107383. doi: 10.1016/j.cnsns.2023.107383

Author

Bashkirtseva, Irina ; Pisarchik, Alexander N. ; Ryashko, Lev. / Multistability and stochastic dynamics of Rulkov neurons coupled via a chemical synapse. In: Communications in Nonlinear Science and Numerical Simulation. 2023 ; Vol. 125.

BibTeX

@article{54269b62776b4ee98ea8466b2e918f43,
title = "Multistability and stochastic dynamics of Rulkov neurons coupled via a chemical synapse",
abstract = "We 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. ",
author = "Irina Bashkirtseva and Pisarchik, {Alexander N.} and Lev Ryashko",
note = "The work was supported by the Russian Science Foundation (project No. 21-11-00062).",
year = "2023",
month = oct,
day = "1",
doi = "10.1016/j.cnsns.2023.107383",
language = "English",
volume = "125",
journal = "Communications in Nonlinear Science and Numerical Simulation",
issn = "1007-5704",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Multistability and stochastic dynamics of Rulkov neurons coupled via a chemical synapse

AU - Bashkirtseva, Irina

AU - Pisarchik, Alexander N.

AU - Ryashko, Lev

N1 - The work was supported by the Russian Science Foundation (project No. 21-11-00062).

PY - 2023/10/1

Y1 - 2023/10/1

N2 - We 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.

AB - We 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.

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85163986624

UR - https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=tsmetrics&SrcApp=tsm_test&DestApp=WOS_CPL&DestLinkType=FullRecord&KeyUT=001029016100001

U2 - 10.1016/j.cnsns.2023.107383

DO - 10.1016/j.cnsns.2023.107383

M3 - Article

VL - 125

JO - Communications in Nonlinear Science and Numerical Simulation

JF - Communications in Nonlinear Science and Numerical Simulation

SN - 1007-5704

M1 - 107383

ER -

ID: 41586122