OSA's Digital Library

Optics Express

Optics Express

  • Editor: Andrew M. Weiner
  • Vol. 21, Iss. 1 — Jan. 14, 2013
  • pp: 12–20

Optoelectronic reservoir computing: tackling noise-induced performance degradation

M. C. Soriano, S. Ortín, D. Brunner, L. Larger, C. R. Mirasso, I. Fischer, and L. Pesquera  »View Author Affiliations


Optics Express, Vol. 21, Issue 1, pp. 12-20 (2013)
http://dx.doi.org/10.1364/OE.21.000012


View Full Text Article

Enhanced HTML    Acrobat PDF (1154 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

We present improved strategies to perform photonic information processing using an optoelectronic oscillator with delayed feedback. In particular, we study, via numerical simulations and experiments, the influence of a finite signal-to-noise ratio on the computing performance. We illustrate that the performance degradation induced by noise can be compensated for via multi-level pre-processing masks.

© 2013 OSA

OCIS Codes
(190.3100) Nonlinear optics : Instabilities and chaos
(200.3050) Optics in computing : Information processing
(250.4745) Optoelectronics : Optical processing devices

ToC Category:
Optics in Computing

History
Original Manuscript: August 15, 2012
Revised Manuscript: November 1, 2012
Manuscript Accepted: December 16, 2012
Published: January 2, 2013

Citation
M. C. Soriano, S. Ortín, D. Brunner, L. Larger, C. R. Mirasso, I. Fischer, and L. Pesquera, "Optoelectronic reservoir computing: tackling noise-induced performance degradation," Opt. Express 21, 12-20 (2013)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-21-1-12


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. J. L. O’Brien, “Optical quantum computing,” Science7, 1567–1570 (2007). [CrossRef]
  2. H. J. Caulfield and S. Dolev, “Why future supercomputing requires optics,” Nat. Photonics4, 261 (2010). [CrossRef]
  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, 2531–2560 (2002). [CrossRef] [PubMed]
  4. H. Jaeger and H. Haas, “Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication,” Science304, 78–80 (2004). [CrossRef] [PubMed]
  5. D. Verstraeten, B. Schrauwen, M. D’Haene, and D. Stroobandt, “An experimental unification of reservoir computing methods,” Neural Networks20, 391–403 (2007). [CrossRef] [PubMed]
  6. M. Rabinovich, R. Huerta, and G. Laurent, “Transient dynamics of neural processing,” Science321, 48–50 (2008). [CrossRef] [PubMed]
  7. W. Maass and H. Markram, “On the computational power of recurrent circuits of spiking neurons,” J. Comput. Syst. Sci.69, 593–616 (2004). [CrossRef]
  8. K. Vandoorne, W. Dierckx, B. Schrauwen, D. Verstraeten, R. Baets, P. Bienstman, and J. Campenhout, “Towards optical signal processing using photonic reservoir computing,” Opt. Express16, 11182—11192 (2008). [CrossRef] [PubMed]
  9. K. Vandoorne, J. Dambre, D. Verstraeten, B. Schrauwen, and P. Bienstman, “Parallel reservoir computing using optical amplifiers,” IEEE Trans. Neural Networks22, 1469–1481 (2011). [CrossRef]
  10. 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,” Nature Commun.2, 468 (2011). [CrossRef]
  11. 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. Express20, 3241–3249 (2012). [CrossRef] [PubMed]
  12. Y. Paquot, F. Duport, A. Smerieri, J. Dambre, B. Schrauwen, M. Haelterman, and S. Massar, “Optoelectronic reservoir computing,” Sci. Rep.2, 287 (2012). [CrossRef] [PubMed]
  13. R. Martinenghi, S. Rybalko, M. Jacquot, Y. K. Chembo, and L. Larger, “Photonic nonlinear transient computing with multiple-delay wavelength dynamics,” Phys. Rev Lett.108, 244101 (2012). [CrossRef] [PubMed]
  14. D. Woods and T. J. Naughton, “Photonic neural networks,” Nature Phys.8, 257–258 (2012). [CrossRef]
  15. J. P. Crutchfield, L. D. William, and S. Sudeshna, “Introduction to focus issue: intrinsic and designed computation: information processing in dynamical systems beyond the digital hegemony,” Chaos20, 037101 (2010). [CrossRef] [PubMed]
  16. J. Dambre, D. Verstraeten, B. Schrauwen, and S. Massar, “Information processing capacity of dynamical systems,” Sci. Rep.2, 514 (2012). [CrossRef] [PubMed]
  17. K. Ikeda, “Multiple-valued stationary state and its instability of the transmitted light by a ring cavity system,” Optics Commun.30, 257–261 (1979). [CrossRef]
  18. L. Larger, J. P. Goedgebuer, and J. M. Merolla, “Chaotic oscillator in wavelength: a new setup for investigating differential difference equations describing nonlinear dynamics,” IEEE J. Quantum Electron.34, 594–601 (1998). [CrossRef]
  19. L. Larger, J.-P. Goedgebuer, and V. S. Udalsov, “Ikeda–based nonlinear delayed dynamics for application to secure optical transmission systems using chaos,” C.R. de Physique5, 669–681 (2004). [CrossRef]
  20. Y. Kouomou Chembo, L. Larger, H. Tavernier, R. Bendoula, E. Rubiola, and P. Colet, “Dynamic instabilities of microwaves generated with optoelectronic oscillators,” Opt. Lett.32, 2571–2573 (2007). [CrossRef] [PubMed]
  21. M. C. Soriano, L. Zunino, L. Larger, I. Fischer, and C. R. Mirasso, “Distinguishing fingerprints of hyperchaotic and stochastic dynamics in optical chaos from a delayed opto-electronic oscillator,” Opt. Lett.36, 2212–2214 (2011). [CrossRef] [PubMed]
  22. A. Rodan and P. Tin̂o, “Minimum complexity echo state network,” IEEE Trans. Neural Networks22, 131–144 (2011). [CrossRef]
  23. U. Huebner, N. B. Abraham, and C. O. Weiss, “Dimensions and entropies of chaotic intensity pulsations in a single-mode far-infrared NH3 laser”, Phys. Rev. A40, 6354–6365 (1989). [CrossRef]
  24. A. S. Weigend and N. A. Gershenfeld, “Time series prediction: forecasting the future and understanding the past,” http://www-psych.stanford.edu/andreas/Time-Series/SantaFe.html (1993).
  25. L. Cao, “Support vector machines experts for time series forecasting”, Neurocomputing51, 321–339 (2003). [CrossRef]

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