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 …

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) have emerged as a biology-inspired method
mimicking the spiking nature of brain neurons. This bio-mimicry derives SNNs' energy …

Modern computation based on von Neumann architecture is now a mature cutting-edge
science. In the von Neumann architecture, processing and memory units are implemented …

Abstract Deep Spiking Neural Networks (SNNs) present optimization difficulties for gradient-
based approaches due to discrete binary activation and complex spatial-temporal dynamics …

Abstract Spiking Neural Network (SNN) is a promising energy-efficient AI model when
implemented on neuromorphic hardware. However, it is a challenge to efficiently train SNNs …

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

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

Abstract Spiking Neural Network (SNN) has been recognized as one of the next generation
of neural networks. Conventionally, SNN can be converted from a pre-trained ANN by only …

As one of the energy-efficient alternatives of conventional neural networks (CNNs), spiking
neural networks (SNNs) have gained more and more interest recently. To train the deep …