Photonic Reservoir Computing : : Optical Recurrent Neural Networks / / ed. by Daniel Brunner, Guy Van der Sande, Miguel C. Soriano.

Photonics has long been considered an attractive substrate for next generation implementations of machine-learning concepts. Reservoir Computing tremendously facilitated the realization of recurrent neural networks in analogue hardware. This concept exploits the properties of complex nonlinear dynam...

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Bibliographic Details
Superior document:Title is part of eBook package: De Gruyter DG Plus eBook-Package 2019
MitwirkendeR:
HerausgeberIn:
Place / Publishing House:Berlin ;, Boston : : De Gruyter, , [2019]
©2019
Year of Publication:2019
Language:English
Online Access:
Physical Description:1 online resource (XIII, 264 p.)
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Description
Other title:Frontmatter --
Preface --
Contents --
List of Contributing Authors --
1. Introduction to novel photonic computing --
2. Information processing and computation with photonic reservoir systems --
3. Integrated on-chip reservoirs --
4. Large scale spatiotemporal reservoirs --
5. Time delay systems for reservoir computing --
6. Ikeda delay dynamics as Reservoir processors --
7. Semiconductor lasers as reservoir substrates --
8. Advanced reservoir computers: analogue autonomous systems and real time control --
Outlook --
Index
Summary:Photonics has long been considered an attractive substrate for next generation implementations of machine-learning concepts. Reservoir Computing tremendously facilitated the realization of recurrent neural networks in analogue hardware. This concept exploits the properties of complex nonlinear dynamical systems, giving rise to photonic reservoirs implemented by semiconductor lasers, telecommunication modulators and integrated photonic chips.
Format:Mode of access: Internet via World Wide Web.
ISBN:9783110583496
9783110719567
9783110616859
9783110610765
9783110664232
9783110610642
9783110606461
DOI:10.1515/9783110583496
Access:restricted access
Hierarchical level:Monograph
Statement of Responsibility: ed. by Daniel Brunner, Guy Van der Sande, Miguel C. Soriano.