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 …

By mimicking the neurons and synapses of the human brain and employing spiking neural
networks on neuromorphic chips, neuromorphic computing offers a promising energy …

Brain-inspired computing (BIC) is an emerging research field that aims to build fundamental
theories, models, hardware architectures, and application systems toward more general …

Abstract Brain-inspired Spiking Neural Networks (SNNs) have bio-plausibility and low-
power advantages over Artificial Neural Networks (ANNs). Applications of SNNs are …

The spiking neural networks (SNNs) that efficiently encode temporal sequences have shown
great potential in extracting audio-visual joint feature representations. However, coupling …

Multi-timestep simulation of brain-inspired Spiking Neural Networks (SNNs) boost memory
requirements during training and increase inference energy cost. Current training methods …

The ambition of brain-inspired Spiking Neural Networks (SNNs) is to become a low-power
alternative to traditional Artificial Neural Networks (ANNs). This work addresses two major …

Introduction The integration of self-attention mechanisms into Spiking Neural Networks
(SNNs) has garnered considerable interest in the realm of advanced deep learning …

Data augmentation is an effective way to overcome the overfitting problem of deep learning
models. However, most existing studies on data augmentation work on framelike data (eg …

Multi-timestep simulation of brain-inspired Spiking Neural Networks (SNNs) boost memory
requirements during training and increase inference energy cost. Current training methods …