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 …

We consider two biologically plausible structures, the Spiking Neural Network (SNN) and the
self-attention mechanism. The former offers an energy-efficient and event-driven paradigm …

Recently, brain-inspired spiking neuron networks (SNNs) have attracted widespread
research interest because of their event-driven and energy-efficient characteristics. Still, it is …

Abstract Spiking Neural Network (SNN), recognized as a type of biologically plausible
architecture, has recently drawn much research attention. It transmits information by $0/1 …

Spiking neural network (SNN) is a new generation of artificial neural networks (ANNs),
which is more analogous with the brain. It has been widely considered with neural …

Abstract Spiking Neural Networks (SNNs) are promising energy-efficient models for
neuromorphic computing. For training the non-differentiable SNN models, the …

Spiking neural networks (SNNs) are a promising energy-efficient alternative to artificial
neural networks (ANNs) due to their rich dynamics, capability to process spatiotemporal …

Spiking neural networks (SNNs) are attracting widespread interest due to their biological
plausibility, energy efficiency, and powerful spatiotemporal information representation …

Abstract The combination of Spiking Neural Networks (SNNs) and Transformers has
attracted significant attention due to their potential for high energy efficiency and high …

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