OSA's Digital Library

Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 20, Iss. 20 — Sep. 24, 2012
  • pp: 22783–22795

All-optical reservoir computing

François Duport, Bendix Schneider, Anteo Smerieri, Marc Haelterman, and Serge Massar  »View Author Affiliations

Optics Express, Vol. 20, Issue 20, pp. 22783-22795 (2012)

View Full Text Article

Enhanced HTML    Acrobat PDF (962 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



Reservoir Computing is a novel computing paradigm that uses a nonlinear recurrent dynamical system to carry out information processing. Recent electronic and optoelectronic Reservoir Computers based on an architecture with a single nonlinear node and a delay loop have shown performance on standardized tasks comparable to state-of-the-art digital implementations. Here we report an all-optical implementation of a Reservoir Computer, made of off-the-shelf components for optical telecommunications. It uses the saturation of a semiconductor optical amplifier as nonlinearity. The present work shows that, within the Reservoir Computing paradigm, all-optical computing with state-of-the-art performance is possible.

© 2012 OSA

OCIS Codes
(060.4370) Fiber optics and optical communications : Nonlinear optics, fibers
(200.4260) Optics in computing : Neural networks
(200.4560) Optics in computing : Optical data processing
(200.4700) Optics in computing : Optical neural systems
(200.4740) Optics in computing : Optical processing

ToC Category:
Optics in Computing

Original Manuscript: July 5, 2012
Revised Manuscript: September 16, 2012
Manuscript Accepted: September 16, 2012
Published: September 20, 2012

François Duport, Bendix Schneider, Anteo Smerieri, Marc Haelterman, and Serge Massar, "All-optical reservoir computing," Opt. Express 20, 22783-22795 (2012)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. H. Jaeger, “The ‘echo state’ approach to analysing and training recurrent neural networks,” Technical Report GMD Report 148 (German National Research Center for Information Technology, 2001).
  2. H. Jaeger, “Short term memory in echo state networks,” GMD Report 152 (German National Research Institute for Computer Science, 2001).
  3. W. Maass, T. Natschläger, and H. Markram, “Real-time computing without stable states: a new framework for neural computation based on perturbations,” Neural Comput. 14(11), 2531–2560 (2002). [CrossRef] [PubMed]
  4. H. Jaeger and H. Haas, “Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication,” Science 304(5667), 78–80 (2004). [CrossRef] [PubMed]
  5. J. J. Steil, “Backpropagation-decorrelation: online recurrent learning with O(N) complexity,” in Proceedings of IEEE International Joint Conference on Neural Networks (IEEE, 2004), pp. 843–848.
  6. R. Legenstein and W. Maass, “What makes a dynamical system computationally powerful?” in New Directions in Statistical Signal Processing: From Systems to Brain (MIT Press, 2005), pp. 127–154.
  7. D. Verstraeten, B. Schrauwen, M. D’Haene, and D. Stroobandt, “An experimental unification of reservoir computing methods,” Neural Netw. 20(3), 391–403 (2007). [CrossRef] [PubMed]
  8. W. Maass, P. Joshi, and E. D. Sontag, “Computational aspects of feedback in neural circuits,” PLOS Comput. Biol. 3(1), e165 (2007). [CrossRef] [PubMed]
  9. H. Jaeger, M. Lukosevicius, D. Popovici, and U. Siewert, “Optimization and applications of echo state networks with leaky-integrator neurons,” Neural Netw. 20(3), 335–352 (2007). [CrossRef] [PubMed]
  10. D. V. Buonomano and W. Maass, “State-dependent computations: spatiotemporal processing in cortical networks,” Nat. Rev. Neurosci. 10(2), 113–125 (2009). [CrossRef] [PubMed]
  11. M. Lukoševičius and H. Jaeger, “Reservoir computing approaches to recurrent neural network training,” Comput. Sci. Rev. 3(3), 127–149 (2009). [CrossRef]
  12. B. Hammer, B. Schrauwen, and J. J. Steil, “Recent advances in efficient learning of recurrent networks”, in Proceedings of the European Symposium on Artificial Neural Networks (2009), pp. 213–216.
  13. F. Triefenbach, A. Jalalvand, B. Schrauwen, and J. Martens, “Phoneme recognition with large hierarchical reservoirs,” Proceedings of Adv.Neural Inf. Processing Syst. 23, 1–9 (2010).
  14. F. Wyffels and B. Schrauwen, “A comparative study of reservoir computing strategies for monthly time series prediction,” Neurocomputing 73(10–12), 1958–1964 (2010). [CrossRef]
  15. M. Lukoševičius, H. Jaeger, and B. Schrauwen, “Reservoir computing trends,” KI - Künstliche Intelligenz, (2012), pp. 1–7.
  16. C. Fernando and S. Sojakka, “Pattern recognition in a bucket,” in Proceedings of the 7th European Conference on Artificial Life2801, W. Banzhaf, J. Ziegler, T. Christaller, P. Dittrich, and J. Kim, eds. (2003), Vol. 2801, pp. 588–597.
  17. F. Schürmann, K. Meier, and J. Schemmel, “Edge of chaos computation in mixed-mode vlsi - a hard liquid,” in Proceedings of Advances in Neural Information Processing Systems, L. K. Saul, Y. Weiss, and Léon, eds. (MIT Press, 2005).
  18. Y. Paquot, B. Schrauwen, J. Dambre, M. Haelterman, and S. Massar, “Reservoir computing: a photonic neural network for information processing,” Proc. SPIE 7728, 77280B, 77280B-12 (2010). [CrossRef]
  19. A. Rodan and P. Tino, “Simple deterministically constructed recurrent neural networks,” Intelligent Data Engineering and Automated Learning (IDEAL, 2010), pp. 267–274.
  20. A. Rodan and P. Tino, “Minimum complexity echo state network,” IEEE Trans. Neural Netw. 22(1), 131–144 (2011). [CrossRef] [PubMed]
  21. L. Appeltant, M. C. Soriano, G. Van der Sande, J. Danckaert, S. Massar, J. Dambre, B. Schrauwen, C. R. Mirasso, and I. Fischer, “Information processing using a single dynamical node as complex system”, Nat. Commun. 2, 468 (2011). http://www.nature.com/ncomms/journal/v2/n9/full/ncomms1476.html
  22. Y. Paquot, F. Duport, A. Smerieri, J. Dambre, B. Schrauwen, M. Haelterman, and S. Massar, “Optoelectronic reservoir computing,” Sci. Rep. 2, 287 (2012). http://www.nature.com/srep/2012/120227/srep00287/full/srep00287.html
  23. L. Larger, M. C. Soriano, D. Brunner, L. Appeltant, J. M. Gutierrez, L. Pesquera, C. R. Mirasso, and I. Fischer, “Photonic information processing beyond turing: an optoelectronic implementation of reservoir computing,” Opt. Express 20(3), 3241–3249 (2012), http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-20-3-3241 . [CrossRef] [PubMed]
  24. K. Vandoorne, W. Dierckx, B. Schrauwen, D. Verstraeten, R. Baets, P. Bienstman, and J. Van Campenhout, “Toward optical signal processing using photonic reservoir computing,” Opt. Express 16(15), 11182–11192 (2008). [CrossRef] [PubMed]
  25. K. Vandoorne, J. Dambre, D. Verstraeten, B. Schrauwen, and P. Bienstman, “Parallel reservoir computing using optical amplifiers,” IEEE Trans. Neural Netw. 22(9), 1469–1481 (2011), http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-16-15-11182 . [CrossRef] [PubMed]
  26. J. Dambre, D. Verstraeten, B. Schrauwen, and S. Massar, “Information processing capacity of dynamical systems,” Sci. Rep. 2, Article number: 514, (2012).
  27. H. Jaeger, “Adaptive nonlinear system identification with echo state networks,” Adv. Neural Inf. Process. Syst. 8, 593–600 (2002).
  28. V. J. Mathews, “Adaptive algorithms for bilinear filtering,” Proc. SPIE 2296(1), 317–327 (1994). [CrossRef]
  29. http://soma.ece.mcmaster.ca/ipix/dartmouth/datasets.html
  30. D. Verstraeten, B. Schrauwen, and D. Stroobandt, “Isolated word recognition using a liquid state machine,” in Proceedings of the 13th European Symposium on Artifcial Neural Networks (ESANN) (2005), pp. 435–440.
  31. Texas Instruments-Developed 46-Word Speaker-Dependent Isolated Word Corpus (TI46), September 1991, NIST Speech Disc 7–1.1 (1 disc) (1991).
  32. R. Lyon, “A computational model of filtering, detection, and compression in the cochlea,” in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (1982), pp. 1282–1285.

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited