The study of dynamics on artificial neurons and neuronal networks is of great significance to
understand brain functions and develop neuromorphic systems. Recently, memristive …

Memristive Hopfield neural network (MHNN) has complex dynamic behavior, which is
suitable for encryption applications. In order to ensure the information security of the medical …

Neural networks have been widely and deeply studied in the field of computational
neurodynamics. However, coupled neural networks and their brain-like chaotic dynamics …

The memristor has received much attention in recent years because of its natural
nonlinearity, but the research on discrete memristor is just beginning. For this reason, a new …

Connecting memristors into any neural circuit can enhance its potential controllability under
external physical stimuli. Memristive current along a magnetic flux-controlled memristor can …

Electromagnetic induction plays a vital role in impacting the neuron dynamics, since the
electromagnetic induction is triggered alongside with the complex interaction of membrane …

In this paper, a new four-dimensional dissipative chaotic system which can produce multiple
asymmetric attractors is designed and its dynamical behaviors are analyzed. The basin of …

A change in neuronal-action potential can generate a magnetically induced current during
the release and propagation of intracellular ions. To better characterize the electromagnetic …

Fractional calculus is closer to reality and has the same memory characteristics as
memristor. Therefore, a fractional-order multistable locally active memristor is proposed for …

Hopfield neural networks are favored by academia and industrial fields due to their
abundant dynamics. In this paper, the dynamical behavior of a small Hopfield neural …