Cortical neurons exhibit several spiking dynamics both in in-vivo and in-vitro experiments.
Neural spikes or action potentials (APs) are also observed in various shapes and forms …

Recurrent neural networks (RNN) have been extensively used to address the problem of
Bayesian inference of a hidden Markov model (HMM). However, such artificial neural …

Objective. This study aims to introduce a novel approach for integrating the post-inhibitory
rebound excitation (PIRE) phenomenon into a neuronal circuit. Excitatory and inhibitory …

Spiking Neural Networks (SNN) along with neuromorphic computing has the advantage of
low power dissipation compared to traditional Von Neumann architectures. Most of the …

This work shows the simulation of a CMOS spiking neuron model that can mimic the
behaviour of a biological neuron for both hyperpolarizing and depolarizing currents. The …

Spiking neural networks (SNN) have gained popularity among the researchers for low
power consumption. An interesting problem on Bayesian inference of hidden Markov …

This paper presents a body-biased silicon neuron circuit which is capable of operating at
ultra-low-voltage supplies and achieves a stable firing frequency. The proposed neuron …

Abstract Spiking Neural Networks use Address Event Representation to communicate
among different Neuron Arrays. To mimic the behavior of the human neural system and …