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Photonic Reservoir Computing

Optical Recurrent Neural Networks

Edited by: Daniel Brunner, Miguel C. Soriano and Guy Van der Sande

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.
  • Author Information

    Daniel Brunner, U Besancon, France;
    Miguel C. Sariano, U Illes Balears, Spain;
    Guy Van der Sande, U Brussels, Belgium.


    "The book is very clearly written by experts in the field and contains numerous experiments and references. It can be used by anyone working in optical computing, which has developed very much in recent years due to optical implementations of neural networks."
    Mircea Dragoman in: OSA. The Optic Society (02.04.2020), https://www.osa-opn.org/home/book_reviews/2020/0420/photonic_reservoir_computing_optical_recurrent_neu/
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    Audience: For researchers and engineers in Physics, Computer Science and Electrical Engineering.