Dynamical complexity of FitzHugh–Nagumo neuron model driven by Lévy noise and Gaussian white noise

Yongfeng Guo, Linjie Wang, Qiang Dong and Xiaojuan Lou

Mathematics and Computers in Simulation (MATCOM), 2021, vol. 181, issue C, 430-443

Abstract: In this paper, on the basis of information theory measures (statistical complexity and normalized Shannon entropy), the dynamical complexity of FitzHugh–Nagumo (FHN) neuron model under the co-excitation of Lévy noise and Gaussian white noise is studied. Because the potential function of the neuron system is asymmetric, we consider not only the total residence time interval of the system, but also the residence time interval of the left and right potential wells respectively. Here, we use Bandt–Pompe algorithm to calculate the three interval sequences, and obtain the statistical complexity and normalized Shannon entropy of the total system as well as the left and right potential wells. Finally, the effects of additive noise intensity, multiplicative noise intensity and system parameter on complexity of system are analyzed. We find that the total dynamical complexity of the system is obviously different from that of a single potential well. In addition, Gaussian white noise and Lévy noise have different effects on the complexity of the system.

Keywords: FHN neural system; Statistical complexity; Normalized Shannon entropy; Lévy noise (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:181:y:2021:i:c:p:430-443

DOI: 10.1016/j.matcom.2020.09.026

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