Chaotic Dynamics of a Two-Neuron Memristive Hopfield Neural Network under Electromagnetic Radiation

Abstract

Electromagnetic radiation (EMR) is a recognized approach for investigating the behavior of the nervous system. This research explores a Hopfield Neural Network (HNN) with two neurons, examining the interplay between synapses and hyperbolic memristors, along with the effects of EMR. By modifying the interference among the networks and adjusting synaptic weights, we can control neuronal capabilities. The study simulates synaptic interference between the two neurons, incorporating parameters of weight and memory, and analyzes how EMR influences chaotic dynamics, complex behavior, transient disturbances, phase portraits, chaotic phenomena, and branching diagrams within these neural networks. This paper investigates how electromagnetic radiation (EMR) influences chaotic dynamics in a two-neuron-based memristive Hopfield neural network (HNN) with synaptic crosstalk. The dynamic behavior of the HNN can be regulated by altering the EMR input to the affected neuron. The proposed model has been simulated using PSpice. The findings demonstrate that external stimuli, represented by EMR, can both enhance complex dynamic behaviors and suppress chaotic patterns by adjusting parameters. Finally, circuit experiments using PSpice confirm the feasibility of the theoretical model, contributing to the control of chaotic phenomena.