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

Since the Lorenz chaotic system was discovered in 1963, the construction of chaotic
systems with complex dynamics has been a research hotspot in the field of chaos. Recently …

The number of attractors in a memristor-based multiscroll Hopfield Neural Network (HNN) is
typically coupled with the number of polynomials, which leads to a coupling between the …

With the development of economic globalization, demands for digital business data sharing
among different subsidiary companies are constantly emerging. How to achieve secure …

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 …

It is believed that local activation is the origin of all complexities, and the locally active
memristive synaptic neural network can generate complex chaotic dynamic behaviors, such …

Memristors are well suited as artificial nerve synapses owing to its unique memory function.
This paper establishes a novel flux-controlled memristor model using hyperbolic function …

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

Activation functions play a crucial in emulating biological neurons within artificial neural
networks. However, the exploration of neural networks composed of various activation …

Memristor possesses synapse-like properties that can mimic excitation and inhibition
between neurons. This article introduces the Sigmoid functions to the memristor and …