Advances in photonic reservoir computing

Guy Van der Sande; Daniel Brunner; Miguel C. Soriano

doi:10.1515/nanoph-2016-0132

journal article Open Access Mar 01, 2017

Advances in photonic reservoir computing

Guy Van der Sande

Daniel Brunner

Miguel C. Soriano

Nanophotonics Vol. 6 No. 3 pp. 561-576 · Wiley

View at Publisher Save 10.1515/nanoph-2016-0132

Abstract

Abstract
We review a novel paradigm that has emerged in analogue neuromorphic optical computing. The goal is to implement a reservoir computer in optics, where information is encoded in the intensity and phase of the optical field. Reservoir computing is a bio‐inspired approach especially suited for processing time‐dependent information. The reservoir’s complex and high‐dimensional transient response to the input signal is capable of universal computation. The reservoir does not need to be trained, which makes it very well suited for optics. As such, much of the promise of photonic reservoirs lies in their minimal hardware requirements, a tremendous advantage over other hardware‐intensive neural network models. We review the two main approaches to optical reservoir computing: networks implemented with multiple discrete optical nodes and the continuous system of a single nonlinear device coupled to delayed feedback.

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Metrics

477

Citations

75

References

Details

Published: Mar 01, 2017
Vol/Issue: 6(3)
Pages: 561-576
License: View

Authors

G

Guy Van der Sande

Applied Physics Research Group (APHY) Vrije Universiteit Brussel (VUB) Pleinlaan 2 1050 Brussels Belgium

D

Daniel Brunner

UMR CNRS FEMTO‐ST 6174/Optics Department Université de Bourgogne Franche‐Comté 15 Avenue des Montboucon F‐25030 Besançon Cedex France

M

Miguel C. Soriano

Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC‐UIB), Campus Universitat de les Illes Balears 07122 Palma de Mallorca Spain

Cite This Article

Guy Van der Sande, Daniel Brunner, Miguel C. Soriano (2017). Advances in photonic reservoir computing. Nanophotonics, 6(3), 561-576. https://doi.org/10.1515/nanoph-2016-0132

Advances in photonic reservoir computing

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