Abstract The famous Hodgkin-Huxley circuit contains two time-varying resistors to describe
the electrophysiological characteristics of sodium and potassium ion channels. But the time …

Spiking Neural Network (SNN), the next generation of Neural Network, is supposed to be
more energy-saving than the previous generation represented by Convolution Neural …

This review investigates the transformative potential of neuromorphic computing in
advancing biointegrated electronics, with a particular emphasis on applications in medical …

Due to the ability to implement customized topology, FPGA is increasingly used to deploy
SNNs in both embedded and high-performance applications. In this paper, we survey state …

The neuron can be called the main cell of a nervous system that can transmit messages from
one neuron to another neuron or another cell through electrical signals. In neuromorphic …

The accurate implementation of biological neural networks, which is one of the important
areas of research in the field of neuromorphic, can be studied in the case of diseases …

Neural circuits with specific structures and diverse neuronal firing features are the
foundation for supporting intelligent tasks in biology and are regarded as the driver for …

As a paradigmatic model for nonlinear dynamics studies, the Hopfield Neural Network
(HNN) demonstrates a high susceptibility to external disturbances owing to its intricate …

In this brief, in order to simplify the implementation of the classical Hodgkin-Huxley neuron
(HH) model, a novel floating memristor emulator is introduced, which consists of four …

Balance among inter-spike interval (ISI), power consumption and biological reality is one of
the major concerns of the Hodgkin-Huxley neuron circuit design trade-off. This paper …