Circuit realization of neural networks is a significant approach in neuromorphic computing.
Researchers have simplified the circuit for single neuron model, but one for neural network …

This paper presents a study on a memristive two-neuron-based Hopfield neural network with
fractional-order derivatives. The equilibrium points of the system are identified, and their …

This paper explores the dynamics and electronic validations of a memristive Helmholtz snap
oscillator (MHSO), employing it to model a process of pseudo-random number generator …

There are numerous studies on Hopfield neural networks with electromagnetic induction
using memristors in either autaptic or synaptic connections. In this study, we explore a novel …

Memristor has been widely regarded as the fourth fundamental circuit element after resistor,
inductor and capacitor, which was theoretically postulated by Chua in 1971 [1] and …

Discrete nonlinear systems usually have higher implementation efficiency than continuous
nonlinear systems, but they can also exhibit dynamics diversity and chaos complex as …

The neuron model has been widely employed in neural-morphic computing systems and
chaotic circuits. This study aims to develop a novel circuit simulation of a three-neuron …

The memristor, a revolutionary electronic component, mimics both neural synapses and
electromagnetic induction phenomena. Recent study challenges are the development of …

To aid researchers in gaining a deeper understanding of the evolutionary laws within the
dynamical system of neural networks, we propose a neural network with variable neuron …

With the expansion of Speech Emotion Recognition in the consumer domain, several
devices, particularly those designed for managing smart home personal assistants for the …