OSA's Digital Library

Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 16, Iss. 15 — Jul. 21, 2008
  • pp: 11182–11192

Toward optical signal processing using Photonic Reservoir Computing

Kristof Vandoorne, Wouter Dierckx, Benjamin Schrauwen, David Verstraeten, Roel Baets, Peter Bienstman, and Jan Van Campenhout  »View Author Affiliations


Optics Express, Vol. 16, Issue 15, pp. 11182-11192 (2008)
http://dx.doi.org/10.1364/OE.16.011182


View Full Text Article

Enhanced HTML    Acrobat PDF (285 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

We propose photonic reservoir computing as a new approach to optical signal processing in the context of large scale pattern recognition problems. Photonic reservoir computing is a photonic implementation of the recently proposed reservoir computing concept, where the dynamics of a network of nonlinear elements are exploited to perform general signal processing tasks. In our proposed photonic implementation, we employ a network of coupled Semiconductor Optical Amplifiers (SOA) as the basic building blocks for the reservoir. Although they differ in many key respects from traditional software-based hyperbolic tangent reservoirs, we show using simulations that such a photonic reservoir can outperform traditional reservoirs on a benchmark classification task. Moreover, a photonic implementation offers the promise of massively parallel information processing with low power and high speed.

© 2008 Optical Society of America

OCIS Codes
(190.4390) Nonlinear optics : Nonlinear optics, integrated optics
(200.4700) Optics in computing : Optical neural systems
(250.5980) Optoelectronics : Semiconductor optical amplifiers

ToC Category:
Optics in computing

History
Original Manuscript: May 8, 2008
Revised Manuscript: June 27, 2008
Manuscript Accepted: July 9, 2008
Published: July 10, 2008

Citation
Kristof Vandoorne, Wouter Dierckx, Benjamin Schrauwen, David Verstraeten, Roel Baets, Peter Bienstman, and Jan Van Campenhout, "Toward optical signal processing using Photonic Reservoir Computing," Opt. Express 16, 11182-11192 (2008)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-16-15-11182


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. H. Jaeger and H. Haas, "Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication," Science 304, 78-80 (2004). [CrossRef] [PubMed]
  2. W. Maass, T. Natschlager and H. Markram, "Real-time computing without stable states: A new framework for neural computation based on perturbations," Neural Computing 14, 2531-2560 (2002). [CrossRef]
  3. W. Maass, T. Natschl¨ager, H. Markram, "A model for real-time computation in generic neural microcircuits," in Proceedings of NIPS, (MIT Press, Vancouver, British Columbia, 2003), pp. 229-236.
  4. M. D. Skowronski, J. G. Harris, "Minimum mean squared error time series classification using an echo state network prediction model," in Proceedings of IEEE International symposium on circuits and systems (Institute of Electrical and Electronics Engineers, Island of Kos, Greece, 2006).
  5. D. Verstraeten, B. Schrauwen, D. Stroobandt, and J. Van Campenhout, "Isolated word recognition with the Liquid State Machine: a case study," Information Processing Lett. 95, 521-528 (2005). [CrossRef]
  6. H. Jaeger, "Reservoir riddles: Suggestions for echo state network research (extended abstract)." in Proceedings of IEEE International Joint Conference on Neural Networks (Institute of Electrical and Electronics Engineers, Montreal, 2005), pp. 1460-1462. [CrossRef]
  7. P. Joshi, W. Maass, "Movement generation and control with generic neural micrrocircuits," in Proceedings of BIO-ADIT (2004), pp. 16-31.
  8. J. J. Steil, "Online stability of backpropagation-decorrelation recurrent learning," Neurocomputing 69, 642-650 (2006). [CrossRef]
  9. H. Y. S. Li, Y. Qiao, and D. Psaltis, "Optical Network for Real-Time Face Recognition," Appl. Opt. 32, 5026-5035 (1993). [CrossRef] [PubMed]
  10. B. Javidi, J. Li, and Q. Tang, "Optical Implementation of Neural Networks for Face Recognition by the use of Nonlinear Joint Transform Correlators," Appl. Opt. 34, 3950-3962 (1995). [CrossRef] [PubMed]
  11. M. Hill, E. Edward, E. Frietman, H. de Waardt, H. J. S. Dorren, and G. Khoe, "All Fiber-Optic Neural Network using coupled SOA based Ring Lasers," IEEE Trans. Neural Networks  13, 1504-1513 (2002). [CrossRef]
  12. C. M. Bishop, Neural Networks for Pattern Recognition (Clarendon Press, Oxford, 1995).
  13. V. N. Vapnik, "An overview of statistical learning theory," IEEE Trans. Neural Networks 10, 988-999 (1999). [CrossRef]
  14. R. Legenstein and W. Maass, "What makes a dynamical system computationally powerful?" in New directions in statistical signal processing: from systems to brain, S. Haykin, ed. (MIT Press, Cambridge, MA, 2007).
  15. Reservoir lab, "Reservoir Computing Toolbox". http://www.elis.ugent.be/rct
  16. D. Verstraeten, B. Schrauwen, M. D�??Haene, and D. Stroobandt, "An experimental unification of reservoir computing methods," Neural Networks 20, 391-403 (2007). [CrossRef] [PubMed]
  17. G. P. Agrawal and N. A. Olsson, "Self-Phase Modulation and Spectral Broadening of Optical Pulses in Semiconductor-Laser Amplifiers," IEEE J. Quantum Electron. 25, 2297-2306 (1989). [CrossRef]
  18. H. S. Rong, Y. H. Kuo, S. B. Xu, A. S. Liu, R. Jones, and M. Paniccia, "Monolithic integrated Raman silicon laser," Opt. Express 14, 6705-6712 (2006), http://www.opticsinfobase.org/abstract.cfm?URI=oe-14-15-6705 [CrossRef] [PubMed]
  19. M. Cernansky and M. Makula, "Feed-forward echo state networks," in Proceedings of IEEE International Joint Conference on Neural Networks (Institute of Electrical and Electronics Engineers, Montreal, 2005), vol. 1473, pp. 1479-1482. [CrossRef]
  20. A. N. Tikhonov and V. I. Arsenin, Solutions of ill-posed problems (Winston & Sons, Washington, 1977).
  21. H. Jaeger, "Adaptive nonlinear system identification with echo state networks," in Proceedings of NIPS, (MIT Press, Cambridge, MA, 2003), pp. 593-600.

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