Neural Processing Letters

, Volume 47, Issue 3, pp 1041–1054 | Cite as

Random Pattern and Frequency Generation Using a Photonic Reservoir Computer with Output Feedback

  • Piotr Antonik
  • Michiel Hermans
  • Marc Haelterman
  • Serge Massar


Reservoir computing is a bio-inspired computing paradigm for processing time dependent signals. The performance of its analogue implementations matches other digital algorithms on a series of benchmark tasks. Their potential can be further increased by feeding the output signal back into the reservoir, which would allow to apply the algorithm to time series generation. This requires, in principle, implementing a sufficiently fast readout layer for real-time output computation. Here we achieve this with a digital output layer driven by a FPGA chip. We demonstrate the first opto-electronic reservoir computer with output feedback and test it on two examples of time series generation tasks: frequency and random pattern generation. We obtain very good results on the first task, similar to idealised numerical simulations. The performance on the second one, however, suffers from the experimental noise. We illustrate this point with a detailed investigation of the consequences of noise on the performance of a physical reservoir computer with output feedback. Our work thus opens new possible applications for analogue reservoir computing and brings new insights on the impact of noise on the output feedback.


Reservoir computing Opto-electronic setup Time series generation FPGA Output feedback 



This study was funded by the Interuniversity Attraction Poles program of the Belgian Science Policy Office (Grant IAP P7-35 “photonics@be”), by the Fonds de la Recherche Scientifique FRS-FNRS and by the Action de Recherche Concertée of the Académie Universitaire Wallonie-Bruxelles (Grant AUWB-2012-12/17-ULB9).

Compliance with Ethical Standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical Standard

This article does not contain any studies with human participants or animals performed by any of the authors.


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Laboratoire d’Information QuantiqueCP 224, Université libre de BruxellesBrusselsBelgium
  2. 2.Service OPERA-PhotoniqueCP 194/5, Université libre de BruxellesBrusselsBelgium

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