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

As one of the energy-efficient alternatives of conventional neural networks (CNNs), spiking
neural networks (SNNs) have gained more and more interest recently. To train the deep …

Abstract Spiking Neural Networks (SNNs) as one of the biology-inspired models have
received much attention recently. It can significantly reduce energy consumption since they …

Abstract Recently, Spiking Neural Networks (SNNs), enjoying extreme energy efficiency,
have drawn much research attention on 2D visual recognition and shown gradually …

Spiking neural networks (SNNs) serve as one type of efficient model to process spatio-
temporal patterns in time series, such as the Address-Event Representation data collected …

The Spiking Neural Network (SNN), as one of the biologically inspired neural network
infrastructures, has drawn increasing attention recently. It adopts binary spike activations to …

Spiking Neural Networks (SNNs) have emerged as energy-efficient alternatives to Artificial
Neural Networks (ANNs) when deployed on neuromorphic chips. While recent studies have …

The non-differentiability of the spike activity has been a hindrance to the development of
high-performance spiking neural networks (SNNs). Current learning algorithms mainly focus …

The next generation of machine intelligence requires the capability of continual learning to
acquire new knowledge without forgetting the old one while conserving limited computing …

Spiking neural networks (SNNs) have attracted intensive attention as a promising energy-
efficient alternative to conventional artificial neural networks (ANNs) recently, which could …