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

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) offer a promising energy-efficient alternative to artificial
neural networks, due to their event-driven spiking computation. However, state-of-the-art …

Abstract Spiking Neural Networks (SNNs) are well known as a promising energy-efficient
alternative to conventional artificial neural networks. Subject to the preconceived impression …

Spiking neural networks (SNNs) have received significant attention for their biological
plausibility. SNNs theoretically have at least the same computational power as traditional …

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

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 …

Spiking Neural Networks (SNNs), as bio-inspired energy-efficient neural networks, have
attracted great attentions from researchers and industry. The most efficient way to train deep …

Abstract Spiking Neural Networks (SNNs) have been studied over decades to incorporate
their biological plausibility and leverage their promising energy efficiency. Throughout …