Photonic Reservoir Computing : : Optical Recurrent Neural Networks /

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:
Andreoli, Louis,
Antonik, Piotr,
Bienstman, Peter,
Brunner, Daniel,
Bueno, Julian,
Dambre, Joni,
Duport, François,
Freiberger, Matthias,
Katumba, Andrew,
Laporte, Floris,
Larger, Laurent,
Lugnan, Alessio,
Ma, Chonghuai,
Maktoobi, Sheler,
Massar, Serge,
Ortín, Silvia,
Pesquera, Luis,
Porte, Xavier,
Sackesyn, Stijn,
Soriano, Miguel C.,
Van der Sande, Guy,
HerausgeberIn:
Brunner, Daniel,
Soriano, Miguel C.,
Van der Sande, Guy,
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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Table of Contents:
  • 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

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