Non-Gaussian noise induced stochastic resonance in FitzHugh–Nagumo neural system with time delay | Zendy

Shenghong Li | Zendy; Jiwei Huang | Zendy

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Non-Gaussian noise induced stochastic resonance in FitzHugh–Nagumo neural system with time delay

Author(s) -

Shenghong Li,

Jiwei Huang

Publication year - 2020

Publication title -

aip advances

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.421

H-Index - 58

ISSN - 2158-3226

DOI - 10.1063/1.5118730

Subject(s) - gaussian noise , stochastic resonance , noise (video) , gaussian , stochastic differential equation , colors of noise , statistical physics , probability density function , mathematics , physics , computer science , algorithm , noise reduction , acoustics , statistics , quantum mechanics , artificial intelligence , image (mathematics)

In this paper, non-Gaussian noise induced stochastic resonance for the FitzHugh–Nagumo neural system with a time delay is investigated. Through the path integral method, the non-Gaussian noise is approximated as a colored noise, and according to the unified colored noise theory and the method of probability density approximation, a stochastic differential equation with a Markovian property is obtained. Then, by applying the two-state theory, the expression of the signal-to-noise ratio (SNR) is derived. Finally, the effects of non-Gaussian noise and time delay parameters in the neural system on the SNR are discussed with the help of analytical results.In this paper, non-Gaussian noise induced stochastic resonance for the FitzHugh–Nagumo neural system with a time delay is investigated. Through the path integral method, the non-Gaussian noise is approximated as a colored noise, and according to the unified colored noise theory and the method of probability density approximation, a stochastic differential equation with a Markovian property is obtained. Then, by applying the two-state theory, the expression of the signal-to-noise ratio (SNR) is derived. Finally, the effects of non-Gaussian noise and time delay parameters in the neural system on the SNR are discussed with the help of analytical results.

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