Abstract
Reservoir computing is a novel paradigm of neural network, offering advantages in low learning cost and ease of implementation as hardware. In this paper we propose a concept of reservoir computing consisting of a semiconductor laser subject to external feedback by a mirror, where input signal is supplied as modulation pattern of mirror reflectivity. In that system, non-linear interaction between optical field and electrons are enhanced in complex manner under substantial external feedback, leading to achieve highly nonlinear projection of input electric signal to output optical field intensity. It is exhibited that the system can most efficiently classify waveforms of sequential input data when operating around laser oscillation’s effective threshold.
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References
Quoc, V.L.: Building high-level features using large scale unsupervised learning. In: 2013 IEEE International Conference on Acoustic, Speech and Signal Processing, pp. 8595–8598, Vancouver, BC (2016)
Taigman, Y., Yang, M., Ranzato, M.A., Wolf, L.: Deep face: closing the gap to human-level performance in face verification. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1708 (2014)
Wang, W., Arora, R., Livescu, K., Bilmes, J.: On deep multi-view representation learning. In: Proceedings of the 32nd International Conference on Machine Learning, pp. 1083–1092 (2015)
Jaeger, H., Haas, H.: Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304, 78–80 (2004)
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)
Verstraeten, D., Schrauwen, B., Stroobandt, D.: Isolated word recognition using a liquid state machine. In: Proceedings of the 13th European Symposium on Artificial Neural Networks (ESANN), pp. 435–440 (2005)
Jalalvand, A., Wallendael, G.V., Walle R.V.: Real-time reservoir computing network-based systems for detection tasks on visual contents. In: 7th International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN), pp. 146–151 (2015)
Triefenbach, F., Jalalvand, A., Schrauwen, B., Martens, J.-P.: Phoneme recognition with large hierarchical reservoirs. Adv. Neural Inf. Process. Syst. 23, 2307–2315 (2010)
Yamane, T., Katayama, Y., Nakane, R., Tanaka, G., Nakano, D.: Wave-based reservoir computing by synchronization of coupled oscillators. In: Arik, S., Huang, T., Lai, W.K., Liu, Q. (eds.) ICONIP 2015. LNCS, vol. 9491, pp. 198–205. Springer, Heidelberg (2015). doi:10.1007/978-3-319-26555-1_23
Appeltant, L., Soriano, M.C., Van der Sande, G., Danchaert, J., Massar, S., Dambre, J., Schrauwen, B., Mirasso, C.R., Fischer, I.: Information processing using a single dynamical node as complex system. Nature Commun. 2, 468–472 (2011)
Nakajima, K., Hauser, H., Li, T., Pfeifers, R.: Information processing via physical soft body. Sci. Rep. 5, 10487 (2015)
Vandoorne, K., Dierckx, W., Schrauwen, B., Verstraeten, D., Baets, R., Bienstman, P., Campenhout, J.V.: Toward optical signal processing using photonic reservoir computing. Opt. Express 16, 1182–1192 (2008)
Brunner, D., Soriano, M.C., Mirasso, C.R., Fischer, I.: Parallel photonic information processing at gigabyte per second data rates using transient state. Nature Commun. 4, 1364 (2012)
Vlasov, Y.: Silicon integrated nanophotonics: From fundamental science to manufacturable technology. http://spie.org/newsroom/pw15_plenary_landing/pw15_plenary_vlasov
Sciamanna, M., Shore, K.A.: Physics and applications of laser diode chaos. Nat. Photonics 9, 151–162 (2015)
Kannno, K., Uchida, A.: Complexity analysis in a semiconductor laser with time-delayed optical feedback. Rev. Laser Eng. 39, 543–549 (2011)
Sukow, D.W., Gauhier, D.J.: Entraining power-dropout events in an external-cavity semiconductor laser using weak modulation of the injection current. IEEE J. Quantum Electron. 36, 175–183 (2000)
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Takeda, S. et al. (2016). Photonic Reservoir Computing Based on Laser Dynamics with External Feedback. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9947. Springer, Cham. https://doi.org/10.1007/978-3-319-46687-3_24
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DOI: https://doi.org/10.1007/978-3-319-46687-3_24
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