Journal Article

All-optical spiking neurosynaptic networks with self-learning capabilities

Bhaskaran, H., Wright, C. D., Feldmann, J., Pernice, W. H. P., Youngblood, N.

2019

Overview

Software implementations of brain-inspired computing underlie many important computational tasks, from image processing to speech recognition, artificial intelligence and deep learning applications. Yet, unlike real neural tissue, traditional computing architectures physically separate the core computing functions of memory and processing, making fast, efficient and low-energy computing difficult to achieve. To overcome such limitations, an attractive alternative is to design hardware that mimics neurons and synapses. Such hardware, when connected in networks or neuromorphic systems, processes information in a way more analogous to brains. Here we present an all-optical version of such a neurosynaptic system, capable of supervised and unsupervised learning. 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. Such photonic neurosynaptic networks promise access to the high speed and high bandwidth inherent to optical systems, thus enabling the direct processing of optical telecommunication and visual data. An optical version of a brain-inspired neurosynaptic system, using wavelength division multiplexing techniques, is presented that is capable of supervised and unsupervised learning.

• Publisher: Nature Publishing Group UK,Nature Publishing Group
• Subject: 142/126 / 639/624/399/1097 / 639/925/927/1021 / Action Potentials / Artificial intelligence / Artificial neural networks / Biomimetics - methods / Brain / Computation / Computational neuroscience / Computer memory / Computer Systems / Computers / Fiber optic equipment / Firing pattern / Frequency division multiplexing / Hardware / Humanities and Social Sciences / Image processing / Information processing / Information systems / Machine learning / Memory / Microprocessors / Models, Neurological / multidisciplinary / Nerve Net - cytology / Nerve Net - physiology / Neural networks / Neural Networks, Computer / Neurons / Neurons - cytology / Neurons - physiology / Object recognition / Optical communication / Optical equipment / Pattern recognition / Pattern Recognition, Automated - methods / Photonics / Photons / Science / Science (multidisciplinary) / Software / Speech recognition / Supervised Machine Learning / Synapses / Synapses - physiology / Unsupervised Machine Learning / Voice recognition / Wave division multiplexing / Wavelength division multiplexing