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Title

Advances in photonic reservoir computing

AuthorsVan der Sande, Guy ; Brunner, Daniel ; Soriano, Miguel C.
KeywordsAnalogue computing
Artificial neural networks
Nonlinear optics
Optical computing
Issue Date12-May-2017
PublisherWalter de Gruyter
CitationNanophotonics 6(3): 561-576 (2017)
AbstractWe 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.
Publisher version (URL)https://doi.org/10.1515/nanoph-2016-0132
URIhttp://hdl.handle.net/10261/173719
DOI10.1515/nanoph-2016-0132
E-ISSN2192-8614
Appears in Collections:(IFISC) Artículos
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