Multistability and Phase Synchronization of Rulkov Neurons Coupled with a Locally Active Discrete Memristor

In order to enrich the dynamic behaviors of discrete neuron models and more effectively mimic biological neural networks, this paper proposes a bistable locally active discrete memristor (LADM) model to mimic synapses. We explored the dynamic behaviors of neural networks by introducing the LADM into two identical Rulkov neurons. Based on numerical simulation, the neural network manifested multistability and new firing behaviors under different system parameters and initial values. In addition, the phase synchronization between the neurons was explored. Additionally, it is worth mentioning that the Rulkov neurons showed synchronization transition behavior; that is, anti-phase synchronization changed to in-phase synchronization with the change in the coupling strength. In particular, the anti-phase synchronization of different firing patterns in the neural network was investigated. This can characterize the different firing behaviors of coupled homogeneous neurons in the different functional areas of the brain, which is helpful to understand the formation of functional areas. This paper has a potential research value and lays the foundation for biological neuron experiments and neuron-based engineering applications.

Automated summary

To enrich the dynamic behaviors of discrete neuron models and mimic biological neural networks more effectively, this paper proposes a bistable locally active discrete memristor (LADM) model to simulate synapses. By introducing the LADM into two identical Rulkov neurons, the dynamic behaviors of neural networks are explored. Numerical simulation shows that the neural network exhibits multistability and new firing behaviors under different system parameters and initial values. In addition, the synchronization between the neurons is also investigated.