As a new brain-inspired computational model of the artificial neural network, a spiking
neural network encodes and processes neural information through precisely timed spike …

Spiking neural networks (SNNs) with a large number of weights and varied weight
distribution can be difficult to implement in emerging in-memory computing hardware due to …

There is a biological evidence to prove information is coded through precise timing of spikes
in the brain. However, training a population of spiking neurons in a multilayer network to fire …

As well known, the huge memory and compute costs of both artificial neural networks
(ANNs) and spiking neural networks (SNNs) greatly hinder their deployment on edge …

Spiking Neural Networks (SNNs) are a promising alternative to traditional deep learning
methods since they perform event-driven information processing. However, a major …

This article presents a novel network, contribution-degree-based spiking neural network
(CDSNN), which combines ideas of spiking neural network (SNN) and fuzzy set theory. In …

The timing of spikes plays an important role in the information processing of brain. However,
for temporal-based learning algorithms, the temporally precise spike as learning target is not …

Spiking neural networks are energy efficient and biological interpretability, communicating
through sparse, asynchronous spikes, which makes them suitable for neuromorphic …

The spiking neural network (SNN) is the third generation of neural networks and performs
remarkably well in cognitive tasks, such as pattern recognition. The temporal neural encode …

This paper presents a new learning algorithm developed for a three layered spiking neural
network for pattern classification problems. The learning algorithm maximizes the interclass …