Tutorial: Photonic neural networks in delay systems

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TitreTutorial: Photonic neural networks in delay systems
Type de publicationJournal Article
Year of Publication2018
AuteursBrunner D., Penkovsky B., Marquez B.A, Jacquot M., Fischer I., Larger L.
JournalJOURNAL OF APPLIED PHYSICS
Volume124
Pagination152004
Date PublishedOCT 21
Type of ArticleArticle
ISSN0021-8979
Résumé

Photonic delay systems have revolutionized the hardware implementation of Recurrent Neural Networks and Reservoir Computing in particular. The fundamental principles of Reservoir Computing strongly facilitate a realization in such complex analog systems. Especially delay systems, which potentially provide large numbers of degrees of freedom even in simple architectures, can efficiently be exploited for information processing. The numerous demonstrations of their performance led to a revival of photonic Artificial Neural Network. Today, an astonishing variety of physical substrates, implementation techniques as well as network architectures based on this approach have been successfully employed. Important fundamental aspects of analog hardware Artificial Neural Networks have been investigated, and multiple high-performance applications have been demonstrated. Here, we introduce and explain the most relevant aspects of Artificial Neural Networks and delay systems, the seminal experimental demonstrations of Reservoir Computing in photonic delay systems, plus the most recent and advanced realizations. Published by AIP Publishing.

DOI10.1063/1.5042342