English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/188491
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE
Exportar a otros formatos:


Tutorial: Photonic neural networks in delay systems

AuthorsBrunner, Daniel ; Penkovsky, B.; Márquez, B. A.; Jacquot, Maxime; Fischer, Ingo ; Larger, Laurent
Issue Date17-Oct-2018
PublisherAmerican Institute of Physics
CitationJournal of Applied Physics 124: 152004 (2018)
AbstractPhotonic 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.
Publisher version (URL)https://doi.org/10.1063/1.5042342
Appears in Collections:(IFISC) Artículos
Files in This Item:
File Description SizeFormat 
photonic_neural_networks_Brunner.pdf1,54 MBAdobe PDFThumbnail
Show full item record
Review this work

WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.