Neuromorphic computing has been attracting ever‐increasing attention due to superior
energy efficiency, with great promise to promote the next wave of artificial general …

Brain-inspired computing technique presents a promising approach to prompt the rapid
development of artificial general intelligence (AGI). As one of the most critical aspects …

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

Spiking neural networks (SNNs) have captivated the attention worldwide owing to their
compelling advantages in low power consumption, high biological plausibility, and strong …

Human brain cortex acts as a rich inspiration source for constructing efficient artificial
cognitive systems. In this paper, we investigate to incorporate multiple brain-inspired …

In this article, we present a spiking neural network (SNN) based on both SRAM processing-
in-memory (PIM) macro and on-chip unsupervised learning with Spike-Time-Dependent …

The paramount concern of highly accurate energy-efficient computing in machines with
significant cognitive capabilities aims to enhance the accuracy and efficiency of bio-inspired …

Neuromorphic devices capable of training spiking neural networks (SNNs) are not easy to
develop due to two main factors: lack of efficient supervised learning algorithms, and high …

Spiking neural networks (SNNs) are more energy-and resource-efficient than artificial neural
networks (ANNs). However, supervised SNN learning is a challenging task due to non …

The technology development greatly increases the amount of digital visual information.
Existing devices cannot efficiently process such huge amounts of data. The technical …