  Software-implementation, via neural networks, of brain-inspired computing
approaches underlie many important modern-day computational tasks, from image
processing to speech recognition, artificial intelligence and deep learning
applications. Yet, differing from real neural tissue, traditional computing
architectures physically separate the core computing functions of memory and
processing, making fast, efficient and low-energy brain-like computing
difficult to achieve. To overcome such limitations, an attractive and
alternative goal is to design direct hardware mimics of brain neurons and
synapses which, when connected in appropriate networks (or neuromorphic
systems), process information in a way more fundamentally analogous to that of
real brains. Here we present an all-optical approach to achieving such a goal.
Specifically, we demonstrate an all-optical spiking neuron device and connect
it, via an integrated photonics network, to photonic synapses to deliver a
small-scale all-optical neurosynaptic system capable of supervised and
unsupervised learning. Moreover, we exploit wavelength division multiplexing
techniques to implement a scalable circuit architecture for photonic neural
networks, successfully demonstrating pattern recognition directly in the
optical domain using a photonic system comprising 140 elements. Such optical
implementations of neurosynaptic networks promise access to the high speed and
bandwidth inherent to optical systems, which would be very attractive for the
direct processing of telecommunication and visual data in the optical domain.
