Dynamics and synchronization of neural models with memristive membranes under energy coupling

Abstract

Dynamical modeling of neural systems plays an important role in explaining and predicting some features of biophysical mechanisms. The electrophysiological environment inside and outside of the nerve cell is different. Due to the continuous and periodical properties of electromagnetic fields in the cell during its operation, electronic components involving two capacitors and a memristor are effective in mimicking these physical features. In this paper, a neural circuit is reconstructed by two capacitors connected by a memristor with periodical mem-conductance. It is found that the memristive neural circuit can present abundant firing patterns without stimulus. The Hamilton energy function is deduced using the Helmholtz theorem. Further, a neuronal network consisting of memristive neurons is proposed by introducing energy coupling. The controllability and flexibility of parameters give the model the ability to describe the dynamics and synchronization behavior of the system.