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

Multiple neuromorphic systems use spiking neural networks (SNNs) to perform computation
in a way that is inspired by concepts learned about the human brain. SNNs are artificial …

Abstract Spiking Neural Networks (SNNs) provide an energy-efficient deep learning option
due to their unique spike-based event-driven (ie, spike-driven) paradigm. In this paper, we …

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices,
and models that contrast the pervasive von Neumann computer architecture. This …

Abstract Spiking Neural Networks (SNNs) are considered promising brain-inspired energy-
efficient models due to their event-driven computing paradigm. The spatiotemporal spike …

Brain-inspired computing (BIC) is an emerging research field that aims to build fundamental
theories, models, hardware architectures, and application systems toward more general …

The explosion of “big data” applications imposes severe challenges of speed and scalability
on traditional computer systems. As the performance of traditional Von Neumann machines …

We propose computing primitive for an all-optical spiking neural network (SNN) based on
vertical-cavity surface-emitting lasers (VCSELs) for supervised learning by using biologically …

We propose a photonic spiking neural network (SNN) consisting of photonic spiking neurons
based on vertical-cavity surface-emitting lasers (VCSELs). The photonic spike timing …

Photonic neuromorphic computing has emerged as a promising approach to building a low-
latency and energy-efficient non-von Neuman computing system. A photonic spiking neural …