Analysis of Attractor Growing of Memristor in FitzHugh-Nagumo Neuron

In this paper, we propose a novel three-dimensional FHN (FitzHugh-Nagumo model) neuron model incorporating a memristor, which is an improvement derived from adding a sinusoidal magnetic conductive memory to the existing FHN neuron model. Employing the ODE45 algorithm, we utilize Lyapunov exponents and bifurcation plots to analyze neuronal activity under different parameters and demonstrate that the improved three-dimensional FHN neuron model with memristor exhibits certain chaotic phenomena under specific conditions. Through the inference analysis of the offset mechanism of the new neuronal model, it is found that it has hidden infinite number of firing modes coexisting. In addition, it has been found that it also has a chaotic attractor growth phenomenon.