Spiking neural networks (SNNs) are bio-inspired neural networks with asynchronous
discrete and sparse characteristics, which have increasingly manifested their superiority in …

The spiking neural network (SNN), as a promising brain-inspired computational model with
binary spike information transmission mechanism, rich spatially-temporal dynamics, and …

Abstract Spiking Neural Network (SNN) is a promising energy-efficient AI model when
implemented on neuromorphic hardware. However, it is a challenge to efficiently train SNNs …

Recently, brain-inspired spiking neuron networks (SNNs) have attracted widespread
research interest because of their event-driven and energy-efficient characteristics. Still, it is …

Spiking Neural Networks (SNNs) are a pathway that could potentially empower low-power
event-driven neuromorphic hardware due to their spatio-temporal information processing …

Abstract Deep Spiking Neural Networks (SNNs) present optimization difficulties for gradient-
based approaches due to discrete binary activation and complex spatial-temporal dynamics …

Developing neuromorphic intelligence on event-based datasets with Spiking Neural
Networks (SNNs) has recently attracted much research attention. However, the limited size …

Spiking Neural Networks (SNNs) are energy efficient alternatives to commonly used deep
neural networks (DNNs). Through event-driven information processing, SNNs can reduce …

Spiking Neural Networks (SNNs) have recently emerged as an alternative to deep learning
owing to sparse, asynchronous and binary event (or spike) driven processing, that can yield …

Different types of dynamics and plasticity principles found through natural neural networks
have been well-applied on Spiking neural networks (SNNs) because of their biologically …