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 …

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

In the field of artificial neural networks, researchers often use the hyperbolic tangent function
as an activation function to imitate the firing rules of biological neurons and to add nonlinear …

Compared with dissipative chaotic systems, conservative chaotic systems are more suitable
for secure communication based on chaos. In this paper, an effective method for …

Memristor and activation function are two important nonlinear factors of the memristive
Hopfield neural network. The effects of different memristors on the dynamics of Hopfield …

The brain is a complex network system in which a large number of neurons are widely
connected to each other and transmit signals to each other. The memory characteristic of …

Episodic memory, as a type of long-term memory (LTM), is used to learn and store the
unique personal experience. Based on the episodic memory biological mechanism, this …

Neurons play an important role in forming behaviors and cognition through synaptic
interactions. When organized into neural networks, these neurons can exhibit complex …

The complexity of memristive chaotic systems determines whether it is suitable for
applications in different subjects. To enhance complexity both in performance and diversity …

This paper presents a novel memristive synapse-coupled ring neural network (MSCRNN)
through introducing a nonvolatile memristor into a three-neuron Hopfield neural network …