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 …

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 …

Biologically plausible learning with neuronal dendrites is a promising perspective to improve
the spike-driven learning capability by introducing dendritic processing as an additional …

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

The Spiking Neural Network (SNN), as one of the biologically inspired neural network
infrastructures, has drawn increasing attention recently. It adopts binary spike activations to …

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 …

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 …