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

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) have ultra-low energy consumption and high biological
plausibility due to their binary and bio-driven nature compared with artificial neural networks …

Artificial neural networks (ANNs) can help camera-based remote photoplethysmography
(rPPG) in measuring cardiac activity and physiological signals from facial videos, such as …

Spiking Neural Networks (SNNs), known for their biologically plausible architecture, face the
challenge of limited performance. The self-attention mechanism, which is the cornerstone of …

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 …

Event camera-based pattern recognition is a newly arising research topic in recent years.
Current researchers usually transform the event streams into images, graphs, or voxels, and …

Spiking neural networks (SNNs) offer an energy-efficient alternative to conventional deep
learning by emulating the event-driven processing manner of the brain. Incorporating …

Event cameras have unique advantages in object detection, capturing asynchronous events
without continuous frames. They excel in dynamic range, low latency, and high-speed …