Memristor initial-offset boosting and its bifurcation mechanism in a memristive FitzHugh-Nagumo neuron model with hidden dynamics

Abstract

Electromagnetic induction plays a vital role in impacting the neuron dynamics, since the electromagnetic induction is triggered alongside with the complex interaction of membrane potential and various ions transport. In other words, the electromagnetic induction is reversely subjected to the membrane potential fluctuation and ions distribution. Flux-controlled memristor described by flux and voltage has been employed to restrict the electromagnetic induction effect. This paper presents a flux-controlled memristor with sinusoidal mem-conductance function and hyperbolic tangent function modulated input. The memristor can availably reflect the non-uniform ions distribution inside and outside neuron membrane. Then, an improved memristive FitzHugh-Nagumo (mFHN) neuron model is built to explore the dynamical effect of the memristive electromagnetic induction. Numerical simulations and theoretical analysis are performed, which reveal that the mFHN neuron model possesses no equilibrium point and can generate abundant hidden dynamics. Interestingly, hidden coexisting behavior is triggered by memristor initial-offset boosting, which evokes the emergence of infinitely hidden coexisting firing patterns. Besides, bifurcation mechanism of the memristor initial-offset boosting behavior is theoretically analyzed. Furthermore, PowerSIM-based (PSIM-based) analog circuit simulations and microcontroller unit based (MCU-based) digital experiments are executed, the captured results perfectly verify the correctness of numerical results and the feasibility of analog/digital hardware experiments.