The human brain is a sophisticated, high‐performance biocomputer that processes multiple
complex tasks in parallel with high efficiency and remarkably low power consumption …

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 …

Neuromorphic computing is henceforth a major research field for both academic and
industrial actors. As opposed to Von Neumann machines, brain-inspired processors aim at …

Signal transmission in the brain relies on voltage-gated ion channels, which exhibit the
electrical behaviour of memristors, resistors with memory. State-of-the-art technologies …

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

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 Deep learning, especially Convolutional Neural Networks (CNNs), has been widely
applied in many domains. The large number of parameters in a CNN allow it to learn …

In the recent years, the spiking neural network (SNN) has attracted increasing attention due
to its low energy consumption and online learning potential. However, the design of SNN …

Abstract Spiking Neural Networks (SNNs) are considered a promising alternative to Artificial
Neural Networks (ANNs) for their event-driven computing paradigm when deployed on …

Thanks to their event-driven nature, spiking neural networks (SNNs) are surmised to be
great computation-efficient models. The spiking neurons encode beneficial temporal facts …