Inverse stochastic resonance in modular neural network with synaptic plasticity

doi:10.1088/1674-1056/aca602

中国物理B ›› 2023, Vol. 32 ›› Issue (3): 30201-030201.doi: 10.1088/1674-1056/aca602

Inverse stochastic resonance in modular neural network with synaptic plasticity

Yong-Tao Yu(于永涛) and Xiao-Li Yang(杨晓丽)^†

School of Mathematics and Statistics, Shaanxi Normal University, Xi'an 710062, China

收稿日期:2022-09-03 修回日期:2022-11-12 接受日期:2022-11-25 出版日期:2023-02-14 发布日期:2023-02-21
通讯作者: Xiao-Li Yang E-mail:yangxiaoli@snnu.edu.cn
基金资助:
Project supported by the National Natural Science Foundation of China (Grant No. 11972217).

Inverse stochastic resonance in modular neural network with synaptic plasticity

Yong-Tao Yu(于永涛) and Xiao-Li Yang(杨晓丽)^†

School of Mathematics and Statistics, Shaanxi Normal University, Xi'an 710062, China

Received:2022-09-03 Revised:2022-11-12 Accepted:2022-11-25 Online:2023-02-14 Published:2023-02-21
Contact: Xiao-Li Yang E-mail:yangxiaoli@snnu.edu.cn
Supported by:
Project supported by the National Natural Science Foundation of China (Grant No. 11972217).

摘要/Abstract

摘要： 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.

关键词: inverse stochastic resonance, synaptic plasticity, modular neural network

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.

Key words: inverse stochastic resonance, synaptic plasticity, modular neural network

中图分类号: (Probability theory, stochastic processes, and statistics)

02.50.-r

05.40.-a (Fluctuation phenomena, random processes, noise, and Brownian motion) 87.85.dq (Neural networks) 02.30.Ks (Delay and functional equations)

Yong-Tao Yu(于永涛) and Xiao-Li Yang(杨晓丽). Inverse stochastic resonance in modular neural network with synaptic plasticity[J]. 中国物理B, 2023, 32(3): 30201-030201.

Yong-Tao Yu(于永涛) and Xiao-Li Yang(杨晓丽). Inverse stochastic resonance in modular neural network with synaptic plasticity[J]. Chin. Phys. B, 2023, 32(3): 30201-030201.

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Inverse stochastic resonance in modular neural network with synaptic plasticity

Inverse stochastic resonance in modular neural network with synaptic plasticity

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