Toward optical signal processing using photonic reservoir computing

Optics Express

Volumn 16, Issue 15, 2008, Pages 11182-11192

  Vandoorne, Kristof ^a   Dierckx, Wouter ^a   Schrauwen, Benjamin ^a   Verstraeten, David ^a   Baets, Roel ^a   Bienstman, Peter ^a   Van Campenhout, Jan ^a  

^a GHENT UNIVERSITY   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

LIGHT AMPLIFIERS; OPTICAL SIGNAL PROCESSING; PATTERN RECOGNITION; SEMICONDUCTOR OPTICAL AMPLIFIERS;

BASIC BUILDING BLOCK; BENCHMARK CLASSIFICATION; HYPERBOLIC TANGENT; MASSIVELY PARALLELS; NEW APPROACHES; NONLINEAR ELEMENTS; PHOTONIC RESERVOIR COMPUTING; RESERVOIR COMPUTING;

SIGNAL PROCESSING;

ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER AIDED DESIGN; COMPUTER SIMULATION; EQUIPMENT; EQUIPMENT DESIGN; INSTRUMENTATION; LIGHT; OPTICS; PHOTON; RADIATION SCATTERING; SEMICONDUCTOR; SIGNAL PROCESSING; THEORETICAL MODEL;

COMPUTER SIMULATION; COMPUTER-AIDED DESIGN; EQUIPMENT DESIGN; EQUIPMENT FAILURE ANALYSIS; LIGHT; MODELS, THEORETICAL; NEURAL NETWORKS (COMPUTER); OPTICS; PHOTONS; SCATTERING, RADIATION; SEMICONDUCTORS; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 47849114096     PISSN: None     EISSN: 10944087     Source Type: Journal    
DOI: 10.1364/OE.16.011182     Document Type: Article

References (21)

1. H. Jaeger and H. Haas, "Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication," Science 304, 78-80 (2004).
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).
3. W. Maass, T. Natschlager, 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).
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.
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).
9. H. Y. S. Li, Y. Qiao, and D. Psaltis, "Optical Network for Real-Time Face Recognition," Appl. Opt. 32, 5026-5035 (1993).
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).
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).
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).
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).
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).
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.
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), pp. 1479-1482 vol. 1473.
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.