Dynamical Complexity of FHN Neuron System Driven by Correlated Noises and Periodic Signal

In this paper, the dynamical complexity of FHN neuron system under the co-excitation of correlated noises and periodic signals is studied by means of information theory measures. Based on the definition of statistical complexity and normalized Shannon entropy, as well as the Bandt–Pompe algorithm, the total statistical complexity and the total normalized Shannon entropy of the FHN neuron system are calculated. Because the potential function of the system is asymmetric, we also calculate the statistical complexity and the normalized Shannon entropy in two different potential wells respectively. Moreover, the effects of additive noise intensity, multiplicative noise intensity, noise correlation time, cross-correlation strength and amplitude on dynamical complexity are analyzed.