Open AccessMean response time of FitzHugh-Nagumo model in the presence of non-Gaussian noise and a periodic signal
Author(s) -
Chao Xu,
Yanmei Kang
Publication year - 2011
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.60.108701
Subject(s) - gaussian noise , noise (video) , physics , stochastic resonance , additive white gaussian noise , white noise , gaussian , statistical physics , signal (programming language) , stability (learning theory) , biological neuron model , neuron , mathematics , computer science , quantum mechanics , algorithm , statistics , neuroscience , biology , artificial intelligence , machine learning , image (mathematics) , programming language
The dynamics of the FitzHugh-Nagumo (FHN) model in the presence of non-Gaussian noise and a periodic signal is analyzed in this paper. We observe the resonant activation (RA) and the noise enhanced stability (NES) phenomena and analyze the effect of the non-Gaussian noise on the neuron dynamics by the mean response time (MRT) of the neuron. Some significant changes of the resonant activation (RA) and noise enhanced stability (NES) phenomena due to the correlation time of the noise are found. We observe that the NES effect is suppressed and RA phenomenon is unchanged, i.e., the non-Gaussian noise effectively enhances the efficiency of the neuronal response, for the case of strongly correlated noise. We report on the MRT as a function of q, and find that MRT is nonmonotonicaly dependent on q with a minimum at a finite q value which is smaller than 1. Finally we obtain that in certain situations, the non-Gaussian noise causes rescaling phenomenon, then the effect of non-Gaussian noise can be reproduced by a white noise.