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ISSN:2164-6457 (print)
ISSN:2164-6473 (online)
Journal of Applied Nonlinear Dynamics
Miguel A. F. Sanjuan (editor), Albert C.J. Luo (editor)
Miguel A. F. Sanjuan (editor)

Department of Physics, Universidad Rey Juan Carlos, 28933 Mostoles, Madrid, Spain

Email: miguel.sanjuan@urjc.es

Albert C.J. Luo (editor)

Department of Mechanical and Industrial Engineering, Southern Illinois University Ed-wardsville, IL 62026-1805, USA

Fax: +1 618 650 2555 Email: aluo@siue.edu

The Stochastic Multiresonance Phenomenon in Excitatory-Inhibitory Neuronal Network

Journal of Applied Nonlinear Dynamics 14(1) (2025) 129--139 | DOI:10.5890/JAND.2025.03.008

Changzhi Xie$^1$, Huiyan Li$^2$, Xiaojuan Sun$^{3,4\dag}$

$^1$ School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China

$^2$ School of Information, Beijing Wuzi University, Beijing, China

$^3$ School of Science, Beijing University of Posts and Telecommunications, Beijing, China

$^4$ Key Laboratory of Mathematics and Information Networks (Beijing University of Posts and Telecommunica- tions), Ministry of Education, China

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Abstract

In biological neuronal systems, information propagation and processing are based on signal detection. Stochastic resonance (SR) is an universal phenonmenon for detecting information in nonlinear dynamic systems. In this paper, we investigate the influence of coupling strength from inhibitory neurons on stochastic resonance (SR) in small-world neuronal networks. Each neuron is described as FitzHugh-Nagumo (FHN) model. The main results report that multiple optimal noise could induce stochastic resonance (SR), which is referred as stochastic multiresonance(SMR), and inhibitory neurons could either destroy stochastic multiresonance(SMR) or turn stochastic multiresonance(SMR) to stochastic resonance(SR) in neuronal network.

Acknowledgments

This study was financially supported by the National Natural Science Foundation of China (Grant No. 12072046).

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