Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving a lot of
attention lately due to its promise of reducing the computational energy, latency, as well as …

In the era of information, characterized by an exponential growth in data volume and an
escalating level of data abstraction, there has been a substantial focus on brain‐like chips …

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 …

Abstract Spiking Neural Networks (SNNs) provide an energy-efficient deep learning option
due to their unique spike-based event-driven (ie, spike-driven) paradigm. In this paper, we …

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 …

Brain-inspired spiking neural networks (SNNs) are becoming a promising energy-efficient
alternative to traditional artificial neural networks (ANNs). However, the performance gap …

The biologically inspired spiking neurons used in neuromorphic computing are nonlinear
filters with dynamic state variables—very different from the stateless neuron models used in …

The field of neuromorphic computing promises extremely low-power and low-latency
sensing and processing. Challenges in transferring learning algorithms from traditional …

Event-based visual, a new visual paradigm with bio-inspired dynamic perception and μ s
level temporal resolution, has prominent advantages in many specific visual scenarios and …

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