Abstract
Reservoir Computing is a bio-inspired computing paradigm for processing time dependent signals. We have recently reported the first opto-electronic reservoir computer trained online by an FPGA chip. This setup makes it in principle possible to feed the output signal back into the reservoir, which in turn allows to tackle complex prediction tasks in hardware. In present work, we investigate numerically the performance of an offline-trained opto-electronic reservoir computer with output feedback on four signal generation tasks. We report very good results and show the potential of such setup to be used as a high-speed analog control system.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
The 2006, 07 forecasting competition for neural networks & computational intelligence (2006). http://www.neural-forecasting-competition.com/NN3/. Accessed 21 Feb 2014
Antonik, P., Duport, F., Smerieri, A., Hermans, M., Haelterman, M., Massar, S.: Online training of an opto-electronic reservoir computer. In: Arik, S., Huang, T., Lai, W.K., Liu, Q. (eds.) ICONIP 2015. LNCS, vol. 9490, pp. 233–240. Springer, Heidelberg (2015). doi:10.1007/978-3-319-26535-3_27
Antonik, P., Hermans, M., Duport, F., Haelterman, M., Massar, S.: Towards pattern generation and chaotic series prediction with photonic reservoir computers. In: SPIE’s 2016 Laser Technology and Industrial Laser Conference, vol. 9732 (2016)
Appeltant, L., Soriano, M.C., Van der Sande, G., Danckaert, J., Massar, S., Dambre, J., Schrauwen, B., Mirasso, C.R., Fischer, I.: Information processing using a single dynamical node as complex system. Nat. Commun. 2, 468 (2011)
Brunner, D., Soriano, M.C., Mirasso, C.R., Fischer, I.: Parallel photonic information processing at gigabyte per second data rates using transient states. Nat. Commun. 4, 1364 (2012)
Duport, F., Schneider, B., Smerieri, A., Haelterman, M., Massar, S.: All-optical reservoir computing. Opt. Express 20, 22783–22795 (2012)
Hammer, B., Schrauwen, B., Steil, J.J.: Recent advances in efficient learning of recurrent networks. In: Proceedings of the European Symposium on Artificial Neural Networks, pp. 213–216, Bruges, Belgium, April 2009
Haynes, N.D., Soriano, M.C., Rosin, D.P., Fischer, I., Gauthier, D.J.: Reservoir computing with a single time-delay autonomous Boolean node. Phys. Rev. E 91(2), 020801 (2015)
Ijspeert, A.J.: Central pattern generators for locomotion control in animals and robots: a review. Neural Netw. 21(4), 642–653 (2008)
Jaeger, H.: Short term memory in echo state networks. Technical GMD report 152 (2001)
Jaeger, H., Haas, H.: Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304, 78–80 (2004)
Larger, L., Soriano, M., Brunner, D., Appeltant, L., Gutiérrez, J.M., Pesquera, L., Mirasso, C.R., Fischer, I.: Photonic information processing beyond turing: an optoelectronic implementation of reservoir computing. Opt. Express 20, 3241–3249 (2012)
Lukoševičius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Comp. Sci. Rev. 3, 127–149 (2009)
Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14, 2531–2560 (2002)
Martinenghi, R., Rybalko, S., Jacquot, M., Chembo, Y.K., Larger, L.: Photonic nonlinear transient computing with multiple-delay wavelength dynamics. Phys. Rev. Let. 108, 244101 (2012)
Paquot, Y., Duport, F., Smerieri, A., Dambre, J., Schrauwen, B., Haelterman, M., Massar, S.: Optoelectronic reservoir computing. Sci. Rep. 2, 287 (2012)
Sussillo, D., Abbott, L.: Generating coherent patterns of activity from chaotic neural networks. Neuron 63(4), 544–557 (2009)
Triefenbach, F., Jalalvand, A., Schrauwen, B., Martens, J.P.: Phoneme recognition with large hierarchical reservoirs. Adv. Neural Inf. Process. Syst. 23, 2307–2315 (2010)
Vandoorne, K., Mechet, P., Van Vaerenbergh, T., Fiers, M., Morthier, G., Verstraeten, D., Schrauwen, B., Dambre, J., Bienstman, P.: Experimental demonstration of reservoir computing on a silicon photonics chip. Nat. Commun. 5, 3541 (2014)
Vinckier, Q., Duport, F., Smerieri, A., Vandoorne, K., Bienstman, P., Haelterman, M., Massar, S.: High-performance photonic reservoir computer based on a coherently driven passive cavity. Optica 2(5), 438–446 (2015)
Wyffels, F., Li, J., Waegeman, T., Schrauwen, B., Jaeger, H.: Frequency modulation of large oscillatory neural networks. Biol. Cybern. 108(2), 145–157 (2014)
Acknowledgements
We acknowledge financial support by Interuniversity Attraction Poles program of the Belgian Science Policy Office under grant IAP P7-35 “photonics@be”, by the Fonds de la Recherche Scientifique FRS-FNRS and by the Action de la Recherche Concertée of the Académie Universitaire Wallonie-Bruxelles under grant AUWB-2012-12/17-ULB9.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Antonik, P., Hermans, M., Haelterman, M., Massar, S. (2016). Towards Adjustable Signal Generation with Photonic Reservoir Computers. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9886. Springer, Cham. https://doi.org/10.1007/978-3-319-44778-0_44
Download citation
DOI: https://doi.org/10.1007/978-3-319-44778-0_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44777-3
Online ISBN: 978-3-319-44778-0
eBook Packages: Computer ScienceComputer Science (R0)