The mean first-passage time in simplified FitzHugh-Nagumo neural model driven by correlated non-Gaussian noise and Gaussian noise

被引:11
|
作者
Guo, Yong-Feng [1 ]
Xi, Bei [1 ]
Wei, Fang [1 ]
Tan, Jian-Guo [1 ]
机构
[1] Tianjin Polytech Univ, Sch Sci, Tianjin 300387, Peoples R China
来源
MODERN PHYSICS LETTERS B | 2018年 / 32卷 / 28期
基金
中国国家自然科学基金;
关键词
FitzHugh-Nagumo neural model; stationary probability distribution; mean first-passage time; non-Gaussian noise; BISTABLE-SYSTEM DRIVEN; 1ST PASSAGE TIME; STOCHASTIC RESONANCE; PERIODIC SIGNAL; COLORED-NOISE; ADDITIVE NOISE; KINETIC-MODEL; APPROXIMATION; NEURONS; DIFFUSION;
D O I
10.1142/S0217984918503396
中图分类号
O59 [应用物理学];
学科分类号
摘要
In this paper, the mean first-passage time (MFPT) in simplified FitzHugh-Nagumo (FHN) neural model driven by correlated multiplicative non-Gaussian noise and additive Gaussian white noise is studied. Firstly, using the path integral approach and the unified colored-noise approximation (UCNA), the analytical expression of the stationary probability distribution (SPD) is derived, and the validity of the approximation method employed in the derivation is checked by performing numerical simulation. Secondly, the expression of the MFPT of the system is obtained by applying the definition and the steepest-descent method. Finally, the effects of the multiplicative noise intensity D, the additive noise intensity Q, the noise correlation time tau, the cross-correlation strength lambda and the non-Gaussian noise deviation parameter q on the MFPT are discussed.
引用
收藏
页数:14
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