Stochastic resonance induced by Gaussian white noise and Levy noise in simplified FitzHugh-Nagumo neural system

被引：13

作者：

Guo, Yongfeng ^{[1
]}

Wang, Linjie ^{[1
]}

Wei, Fang ^{[1
]}

Tan, Jianguo ^{[1
]}

机构：

[1] Tianjin Polytech Univ, Sch Math Sci, Tianjin 300387, Peoples R China

来源：

INDIAN JOURNAL OF PHYSICS | 2020年 / 94卷 / 10期

基金：

中国国家自然科学基金;

关键词：

FHN neural system; Levy noise; Stochastic resonance; Signal-to-noise ratio; PERIODIC SIGNAL; PATTERN-FORMATION; DYNAMICS; MODEL;

D O I：

10.1007/s12648-019-01606-4

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

This paper aims to investigate the stochastic resonance (SR) of simplified FitzHugh-Nagumo (FHN) neuron system driven by Gaussian white noise and Levy noise. The Levy noise is generated by Janicki-Weron algorithm, and the numerical solutions of system equation are obtained by the fourth-order stochastic Runge-Kutta algorithm. Then, the SR is determined by the classical measure of signal-to-noise ratio (SNR). Finally, the effects of the Gaussian white noise, Levy noise and system parameters on SNR are discussed by means of numerical simulation. The results show that the larger stability index and skewness parameter are conducive to enhance signal response of FHN neural system; on the contrary, the increase in amplitude A and system parameter c weakens the occurrence of SR.

引用

页码：1625 / 1632

页数：8

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