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

Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic
chips with high energy efficiency by introducing neural dynamics and spike properties. As …

Neuromorphic vision sensor is a new bio-inspired imaging paradigm that reports
asynchronous, continuously per-pixel brightness changes called'events' with high temporal …

Spiking neural network (SNN) has been viewed as a potential candidate for the next
generation of artificial intelligence with appealing characteristics such as sparse …

The training of multilayer spiking neural networks (SNNs) using the error backpropagation
algorithm has made significant progress in recent years. Among the various training …

Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial
neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal …

Spiking neural networks (SNN), also often referred to as the third generation of neural
networks, carry the potential for a massive reduction in memory and energy consumption …

Event-based cameras are raising interest within the computer vision community. These
sensors operate with asynchronous pixels, emitting events, or “spikes”, when the luminance …

Spiking neural networks (SNNs) have shown much promise as an energy-efficient
alternative to artificial neural networks (ANNs). Such methods trained by surrogate gradient …

Abstract Spiking Neural Networks (SNN) are recognised as well-suited for processing
spatiotemporal information with ultra-low energy consumption. However, proposals based …