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

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

Spiking neural networks (SNNs) are regarded as a promising candidate to deal with the
major challenges of current machine learning techniques, including the high energy …

Abstract Spiking Neural Networks (SNNs) have gained huge attention as a potential energy-
efficient alternative to conventional Artificial Neural Networks (ANNs) due to their inherent …

Abstract Spiking Neural networks (SNNs) represent and transmit information by
spatiotemporal spike patterns, which bring two major advantages: biological plausibility and …

Abstract Spiking Neural Networks (SNNs) have recently emerged as a new generation of
low-power deep neural networks, which is suitable to be implemented on low-power …

Spiking neural networks (SNNs) have recently emerged as the low-power alternative to
artificial neural networks (ANNs) because of their sparse, asynchronous, and binary event …

Spiking neural networks (SNNs) have shown advantages in computation and energy
efficiency over traditional artificial neural networks (ANNs) thanks to their event-driven …

The spiking neural network (SNN) mimics the information-processing operation in the
human brain. Directly applying backpropagation to the training of the SNN still has a …

Spiking neural networks (SNNs) are promising in energy-efficient brain-inspired devices for
their rich spatio-temporal dynamics, bio-plausible encoding, and event-driven information …