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

Abstract Spiking Neural Networks (SNNs) are considered promising brain-inspired energy-
efficient models due to their event-driven computing paradigm. The spatiotemporal spike …

Abstract Spiking Neural Networks (SNNs) offer a highly promising computing paradigm due
to their biological plausibility, exceptional spatiotemporal information processing capability …

Abstract Spiking Neural Networks (SNNs) have attracted significant attention due to their
energy-efficient properties and potential application on neuromorphic hardware. State-of-the …

The remarkable success of Vision Transformers in Artificial Neural Networks (ANNs) has led
to a growing interest in incorporating the self-attention mechanism and transformer-based …

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

Brain-inspired spiking neural networks (SNNs) have gained prominence in the field of
neuromorphic computing owing to their low energy consumption during feedforward …

The adaptive leaky integrate-and-fire (ALIF) model is fundamental within computational
neuroscience and has been instrumental in studying our brains $\textit {in silico} $. Due to …

The biological neurons use precise spike times, in addition to the spike firing rate, to
communicate with each other. The time-to-first-spike (TTFS) coding is inspired by such …

Spiking Neural Networks (SNNs) have garnered considerable attention due to their energy
efficiency and unique biological characteristics. However, the widely adopted Leaky …