Memristive electromagnetic induction effects on Hopfield neural network

Due to the existence of membrane potential differences, electromagnetic induction flows can be induced in interconnected neurons Hopfield neural network (HNN). To express flows, this paper presents a unified memristive HNN model using hyperbolic-type memristors link neurons. By employing theoretical analysis along with multiple numerical methods, we explore effects on three Three cases are classified and discussed. When one memristor two bidirectionally, coexisting bifurcation behaviors extreme events disclosed respect coupling strength. neurons, antimonotonicity phenomena periodic chaotic bubbles yielded, initial-related emerged. end end, owning prominent riddled basins attraction demonstrated. In addition, develop printed circuit board (PCB)-based hardware experiments by synthesizing HNN, experimental results well confirm effects. Certainly, PCB-based implementation will benefit integrated design for large-scale future.