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 (SNNs) are brain-inspired energy-efficient models that encode
information in spatiotemporal dynamics. Recently, deep SNNs trained directly have shown …

In recent years, spiking neural networks (SNNs) have received extensive attention in brain-
inspired intelligence due to their rich spatially-temporal dynamics, various encoding …

Spiking neural networks (SNNs) serve as a promising computational framework for
integrating insights from the brain into artificial intelligence (AI). Existing software …

Abstract Brain-inspired Spiking Neural Networks (SNNs) have bio-plausibility and low-
power advantages over Artificial Neural Networks (ANNs). Applications of SNNs are …

Spiking neural networks (SNNs) are considered an attractive option for edge-side
applications due to their sparse, asynchronous and event-driven characteristics. However …

Spiking Neural Networks (SNNs) have attracted significant attention for their energy-efficient
and brain-inspired event-driven properties. Recent advancements, notably Spiking-YOLO …

The ambition of brain-inspired Spiking Neural Networks (SNNs) is to become a low-power
alternative to traditional Artificial Neural Networks (ANNs). This work addresses two major …

We increasingly rely on deep learning algorithms to process colossal amount of
unstructured visual data. Commonly, these deep learning algorithms are deployed as …

Abstract Brain-inspired Spiking Neural Networks (SNNs) have attracted much attention due
to their event-based computing and energy-efficient features. However, the spiking all-or …