Dynamical complexity of FitzHugh-Nagumo neuron model driven by Levy noise and Gaussian white noise

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 Levy 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 Levy noise have different effects on the complexity of the system. (C) 2020 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

Automated summary

In this paper, the dynamical complexity of the FitzHugh-Nagumo neuron model under the co-excitation of Levy noise and Gaussian white noise is studied using information theory measures. The effects of additive noise intensity, multiplicative noise intensity, and system parameter on system complexity are analyzed, revealing differences in complexity between total system dynamics and individual potential wells. Gaussian white noise and Levy noise also have distinct effects on system complexity.