The spiking neural networks (SNNs) are the third generation of artificial neural networks,
which have made great achievements in the field of pattern recognition. However, the …

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

Spiking neural networks (SNNs) are promising in a bio-plausible coding for spatio-temporal
information and event-driven signal processing, which is very suited for energy-efficient …

In recent years, deep learning has revolutionized the field of machine learning, for computer
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …

Abstract Spiking Neural Networks (SNNs) have emerged as a biology-inspired method
mimicking the spiking nature of brain neurons. This bio-mimicry derives SNNs' energy …

Spiking neural networks (SNNs) have advantages in latency and energy efficiency over
traditional artificial neural networks (ANNs) due to their event-driven computation …

Spiking neural networks provide an alternative solution to conventional artificial neural
networks with energy-saving and high-efficiency characteristics after hardware implantation …

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

The brain-inspired and event-driven Spiking Neural Network (SNN) aims at mimicking the
synaptic activity of biological neurons, which transmits binary spike signals between network …

The training method of Spiking Neural Networks (SNNs) is an essential problem, and how to
integrate local and global learning is a worthy research interest. However, the current …