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

With the adoption of smart systems, artificial neural networks (ANNs) have become
ubiquitous. Conventional ANN implementations have high energy consumption, limiting …

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 …

Abstract Spiking Neural Networks (SNNs) are promising in neuromorphic hardware owing to
utilizing spatio-temporal information and sparse event-driven signal processing. However, it …

Spiking neural networks (SNNs) are well known as the brain-inspired models with high
computing efficiency, due to a key component that they utilize spikes as information units …

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 …

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

Brain-inspired computing technique presents a promising approach to prompt the rapid
development of artificial general intelligence (AGI). As one of the most critical aspects …

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

Vanilla spiking neurons in Spiking Neural Networks (SNNs) use charge-fire-reset neuronal
dynamics, which can only be simulated serially and can hardly learn long-time …