Biologically inspired spiking neural networks are increasingly popular in the field of artificial
intelligence due to their ability to solve complex problems while being power efficient. They …

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 …

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 Networks (SNNs) have been studied over decades to incorporate
their biological plausibility and leverage their promising energy efficiency. Throughout …

Spiking Neural Networks (SNNs) have recently emerged as an alternative to deep learning
owing to sparse, asynchronous and binary event (or spike) driven processing, that can yield …

Various hypotheses of information representation in brain, referred to as neural codes, have
been proposed to explain the information transmission between neurons. Neural coding …

Cyber security is one of the most significant challenges in connected vehicular systems and
connected vehicles are prone to different cybersecurity attacks that endanger passengers' …

Abstract Spiking Neural Networks (SNNs) offer a highly promising computing paradigm due
to their biological plausibility, exceptional spatiotemporal information processing capability …

The training of multilayer spiking neural networks (SNNs) using the error backpropagation
algorithm has made significant progress in recent years. Among the various training …