Photonic Reservoir Computing: Optical Recurrent Neural Networks

✍ Scribed by Daniel Brunner (editor); Miguel C. Soriano (editor); Guy Van der Sande (editor)

Publisher: De Gruyter
Year: 2019
Tongue: English
Leaves: 276
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

Ultra-high speed photonic reservoirs.

Fully implemented analogue photonic neural networks.

Theoretical framework of computing with such devices.

✦ Table of Contents

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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