Inverse stochastic resonance in modular neural network with synaptic plasticity

doi:10.1088/1674-1056/aca602

Abstract This work explores the inverse stochastic resonance (ISR) induced by bounded noise and the multiple inverse stochastic resonance induced by time delay by constructing a modular neural network, where the modified Oja's synaptic learning rule is employed to characterize synaptic plasticity in this network. Meanwhile, the effects of synaptic plasticity on the ISR dynamics are investigated. Through numerical simulations, it is found that the mean firing rate curve under the influence of bounded noise has an inverted bell-like shape, which implies the appearance of ISR. Moreover, synaptic plasticity with smaller learning rate strengthens this ISR phenomenon, while synaptic plasticity with larger learning rate weakens or even destroys it. On the other hand, the mean firing rate curve under the influence of time delay is found to exhibit a decaying oscillatory process, which represents the emergence of multiple ISR. However, the multiple ISR phenomenon gradually weakens until it disappears with increasing noise amplitude. On the same time, synaptic plasticity with smaller learning rate also weakens this multiple ISR phenomenon, while synaptic plasticity with larger learning rate strengthens it. Furthermore, we find that changes of synaptic learning rate can induce the emergence of ISR phenomenon. We hope these obtained results would provide new insights into the study of ISR in neuroscience.

Keywords: inverse stochastic resonance synaptic plasticity modular neural network

Received: 03 September 2022
Revised: 12 November 2022
Accepted manuscript online: 25 November 2022

PACS:	02.50.-r	(Probability theory, stochastic processes, and statistics)
	05.40.-a	(Fluctuation phenomena, random processes, noise, and Brownian motion)
	87.85.dq	(Neural networks)
	02.30.Ks	(Delay and functional equations)

Fund: Project supported by the National Natural Science Foundation of China (Grant No. 11972217).

Corresponding Authors: Xiao-Li Yang
E-mail: yangxiaoli@snnu.edu.cn

Cite this article:

Yong-Tao Yu(于永涛) and Xiao-Li Yang(杨晓丽) Inverse stochastic resonance in modular neural network with synaptic plasticity 2023 Chin. Phys. B 32 030201

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