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Impact of optical coherence on the performance of large-scale spatiotemporal photonic reservoir computing systems

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Abstract

Large-scale spatiotemporal photonic reservoir computer (RC) systems offer remarkable solutions for massively parallel processing of a wide variety of hard real-world tasks. In such systems, neural networks are created by either optical or electronic coupling. Here, we investigate the impact of the optical coherence on the performance of large-scale spatiotemporal photonic RCs by comparing a coherent (optical coupling between the reservoir nodes) and incoherent (digital coupling between the reservoir nodes) RC systems. Although the coherent configuration offers significant reduction on the computational load compared to the incoherent architecture, for image and video classification benchmark tasks, it is found that the incoherent RC configuration outperforms the coherent configuration. Moreover, the incoherent configuration is found to exhibit a larger memory capacity than the coherent scheme. Our results pave the way towards the optimization of implementation of large-scale RC systems.

© 2020 Optical Society of America

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2020 (5)

P. Antonik, N. Marsal, and D. Rontani, “Large-scale spatiotemporal photonic reservoir computer for image classification,” IEEE J. Sel. Top. Quantum Electron. 26(1), 1–12 (2020).
[Crossref]

Y. K. Chembo, “Machine learning based on reservoir computing with time-delayed optoelectronic and photonic systems,” Chaos 30(1), 013111 (2020).
[Crossref]

A. Lugnan, A. Katumba, F. Laporte, M. Freiberger, S. Sackesyn, C. Ma, E. Gooskens, J. Dambre, and P. Bienstman, “Photonic neuromorphic information processing and reservoir computing,” APL Photonics 5(2), 020901 (2020).
[Crossref]

J. Dong, M. Rafayelyan, F. Krzakala, and S. Gigan, “Optical reservoir computing using multiple light scattering for chaotic systems prediction,” IEEE J. Sel. Top. Quantum Electron. 26(1), 1–12 (2020).
[Crossref]

S. Maktoobi, L. Froehly, L. Andreoli, X. Porte, M. Jacquot, L. Larger, and D. Brunner, “Diffractive coupling for photonic networks: how big can we go?” IEEE J. Sel. Top. Quantum Electron. 26(1), 1–8 (2020).
[Crossref]

2019 (6)

A. Katumba, X. Yin, J. Dambre, and P. Bienstman, “A neuromorphic silicon photonics nonlinear equalizer for optical communications with intensity modulation and direct detection,” J. Lightwave Technol. 37(10), 2232–2239 (2019).
[Crossref]

S. Sunada and A. Uchida, “Photonic reservoir computing based on nonlinear wave dynamics at microscale,” Sci. Rep. 9(1), 19078 (2019).
[Crossref]

G. Tanaka, T. Yamane, J.-B. Héroux, R. Nakane, N. Kanazawa, S. Takeda, H. Numata, D. Nakano, and A. Hirose, “Recent advances in physical reservoir computing: A review,” Neural Networks 115, 100–123 (2019).
[Crossref]

K. Lüdge and A. Röhm, “Computing with a camera,” Nat. Mach. Intell. 1(12), 551–552 (2019).
[Crossref]

P. Antonik, N. Marsal, D. Brunner, and D. Rontani, “Human action recognition with a large-scale brain-inspired photonic computer,” Nat. Mach. Intell. 1(11), 530–537 (2019).
[Crossref]

N. Semenova, X. Porte, L. Andreoli, M. Jacquot, L. Larger, and D. Brunner, “Fundamental aspects of noise in analog- hardware neural networks,” Chaos 29(10), 103128 (2019).
[Crossref]

2018 (4)

A. Argyris, J. Bueno, and I. Fischer, “Photonic machine learning implementation for signal recovery in optical communications,” Sci. Rep. 8(1), 8487 (2018).
[Crossref]

K. Takano, C. Sugano, M. Inubushi, K. Yoshimura, S. Sunada, K. Kanno, and A. Uchida, “Compact reservoir computing with a photonic integrated circuit,” Opt. Express 26(22), 29424 (2018).
[Crossref]

F. D. L. Coarer, M. Sciamanna, A. Katumba, M. Freiberger, J. Dambre, P. Bienstman, and D. Rontani, “All-optical reservoir computing on a photonic chip using silicon-based ring resonators,” IEEE J. Sel. Top. Quantum Electron. 24(6), 1–8 (2018).
[Crossref]

J. Bueno, S. Maktoobi, L. Froehly, I. Fischer, M. Jacquot, L. Larger, and D. Brunner, “Reinforcement learning in a large scale photonic recurrent neural network,” Optica 5(6), 756 (2018).
[Crossref]

2017 (3)

R. M. Nguimdo, E. Lacot, O. Jacquin, O. Hugon, G. V. der Sande, and H. G. de Chatellus, “Prediction performance of reservoir computing systems based on diode-pumped erbium doped microchip laser subject to optical feedback,” Opt. Lett. 42(3), 375–378 (2017).
[Crossref]

L. Larger, A. Baylón-Fuentes, R. Martinenghi, V. S. Udaltsov, Y. K. Chembo, and M. Jacquot, “High-speed photonic reservoir computing using a time-delay-based architecture: Million words per second classification,” Phys. Rev. X 7(1), 011015 (2017).
[Crossref]

G. V. der Sande, D. Brunner, and M. C. Soriano, “Advances in photonic reservoir computing,” Nanophotonics 6(3), 561–576 (2017).
[Crossref]

2016 (2)

F. Duport, A. Smerieri, A. Akrout, M. Haelterman, and S. Massar, “Fully analogue photonic reservoir computer,” Sci. Rep. 6(1), 22381 (2016).
[Crossref]

R. M. Nguimdo, G. Verschaffelt, J. Danckaert, and G. V. der Sande, “Reducing the phase sensitivity of laser-based optical reservoir computing systems,” Opt. Express 24(2), 1238 (2016).
[Crossref]

2015 (2)

2014 (1)

K. Vandoorne, P. Mechet, T. V. Vaerenbergh, M. Fiers, G. Morthier, D. Verstraeten, B. Schrauwen, J. Dambre, and P. Bienstman, “Experimental demonstration of reservoir computing on a silicon photonics chip,” Nat. Commun. 5(1), 3541 (2014).
[Crossref]

2013 (1)

D. Brunner, M. C. Soriano, C. R. Mirasso, and I. Fischer, “Parallel photonic information processing at gigabyte per second data rates using transient states,” Nat. Commun. 4(1), 1364 (2013).
[Crossref]

2012 (3)

Y. Paquot, F. Duport, A. Smerieri, J. Dambre, B. Schrauwen, M. Haelterman, and S. Massar, “Optoelectronic reservoir computing,” Sci. Rep. 2(1), 287 (2012).
[Crossref]

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(24), 244101 (2012).
[Crossref]

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. Express 20(3), 3241 (2012).
[Crossref]

2011 (3)

P. Buteneers, D. Verstraeten, T. W. P. Van Mierlo, D. Stroobandt, R. Raedt, H. Hallez, and B. Schrauwen, “Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing,” Artif. Intell. Medicine 53(3), 215–223 (2011).
[Crossref]

L. Appeltant, M. C. Soriano, J. D. G. Van der Sande, S. Massar, J. Dambre, B. Schrauwen, C. R. Mirasso, and I. Fischer, “Information processing using a single dynamical node as complex system,” Nat. Commun. 2(1), 468 (2011).
[Crossref]

S. M. Popoff, G. Lerosey, M. Fink, A. C. Boccara, and S. Gigan, “Controlling light through optical disordered media: transmission matrix approach,” New J. Phys. 13(12), 123021 (2011).
[Crossref]

2009 (1)

M. Lukoševičius and H. Jaeger, “Reservoir computing approaches to recurrent neural network training,” Comput. Sci. Rev. 3(3), 127–149 (2009).
[Crossref]

2008 (1)

E. A. Antonelo, B. Schrauwen, and D. Stroobandt, “Event detection and localization for small mobile robots using reservoir computing,” Neural Networks 21(6), 862–871 (2008).
[Crossref]

2007 (1)

D. Verstraeten, B. Schrauwen, M. D’Haene, and D. Stroobandt, “An experimental unification of reservoir computing methods,” Neural Networks 20(3), 391–403 (2007).
[Crossref]

2004 (1)

H. Jaeger and H. Haas, “Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication,” Science 304(5667), 78–80 (2004).
[Crossref]

2002 (1)

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(11), 2531–2560 (2002).
[Crossref]

Akrout, A.

F. Duport, A. Smerieri, A. Akrout, M. Haelterman, and S. Massar, “Fully analogue photonic reservoir computer,” Sci. Rep. 6(1), 22381 (2016).
[Crossref]

Andreoli, L.

S. Maktoobi, L. Froehly, L. Andreoli, X. Porte, M. Jacquot, L. Larger, and D. Brunner, “Diffractive coupling for photonic networks: how big can we go?” IEEE J. Sel. Top. Quantum Electron. 26(1), 1–8 (2020).
[Crossref]

N. Semenova, X. Porte, L. Andreoli, M. Jacquot, L. Larger, and D. Brunner, “Fundamental aspects of noise in analog- hardware neural networks,” Chaos 29(10), 103128 (2019).
[Crossref]

Antonelo, E. A.

E. A. Antonelo, B. Schrauwen, and D. Stroobandt, “Event detection and localization for small mobile robots using reservoir computing,” Neural Networks 21(6), 862–871 (2008).
[Crossref]

Antonik, P.

P. Antonik, N. Marsal, and D. Rontani, “Large-scale spatiotemporal photonic reservoir computer for image classification,” IEEE J. Sel. Top. Quantum Electron. 26(1), 1–12 (2020).
[Crossref]

P. Antonik, N. Marsal, D. Brunner, and D. Rontani, “Human action recognition with a large-scale brain-inspired photonic computer,” Nat. Mach. Intell. 1(11), 530–537 (2019).
[Crossref]

P. Antonik, N. Marsal, D. Brunner, and D. Rontani, “Bayesian optimisation of large-scale photonic reservoir computers,” arXiv preprint arXiv:2004.02535 (2020).

Appeltant, L.

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. Express 20(3), 3241 (2012).
[Crossref]

L. Appeltant, M. C. Soriano, J. D. G. Van der Sande, S. Massar, J. Dambre, B. Schrauwen, C. R. Mirasso, and I. Fischer, “Information processing using a single dynamical node as complex system,” Nat. Commun. 2(1), 468 (2011).
[Crossref]

Argyris, A.

A. Argyris, J. Bueno, and I. Fischer, “Photonic machine learning implementation for signal recovery in optical communications,” Sci. Rep. 8(1), 8487 (2018).
[Crossref]

Bahi, H. E.

H. E. Bahi, Z. Mahani, A. Zatni, and S. Saoud, “A robust system for printed and handwritten character recognition of images obtained by camera phone,” in Wseas Trans. signal Process, vol. 11 (2015), pp. 9–22.

Baylón-Fuentes, A.

L. Larger, A. Baylón-Fuentes, R. Martinenghi, V. S. Udaltsov, Y. K. Chembo, and M. Jacquot, “High-speed photonic reservoir computing using a time-delay-based architecture: Million words per second classification,” Phys. Rev. X 7(1), 011015 (2017).
[Crossref]

Bengio, Y.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” in Proceedings of the IEEE, vol. 86 (1998), p. 2278.

Bienstman, P.

A. Lugnan, A. Katumba, F. Laporte, M. Freiberger, S. Sackesyn, C. Ma, E. Gooskens, J. Dambre, and P. Bienstman, “Photonic neuromorphic information processing and reservoir computing,” APL Photonics 5(2), 020901 (2020).
[Crossref]

A. Katumba, X. Yin, J. Dambre, and P. Bienstman, “A neuromorphic silicon photonics nonlinear equalizer for optical communications with intensity modulation and direct detection,” J. Lightwave Technol. 37(10), 2232–2239 (2019).
[Crossref]

F. D. L. Coarer, M. Sciamanna, A. Katumba, M. Freiberger, J. Dambre, P. Bienstman, and D. Rontani, “All-optical reservoir computing on a photonic chip using silicon-based ring resonators,” IEEE J. Sel. Top. Quantum Electron. 24(6), 1–8 (2018).
[Crossref]

Q. Vinckier, F. Duport, A. Smerieri, K. Vandoorne, P. Bienstman, M. Haelterman, and S. Massar, “High-performance photonic reservoir computer based on a coherently driven passive cavity,” Optica 2(5), 438–446 (2015).
[Crossref]

K. Vandoorne, P. Mechet, T. V. Vaerenbergh, M. Fiers, G. Morthier, D. Verstraeten, B. Schrauwen, J. Dambre, and P. Bienstman, “Experimental demonstration of reservoir computing on a silicon photonics chip,” Nat. Commun. 5(1), 3541 (2014).
[Crossref]

Boccara, A. C.

S. M. Popoff, G. Lerosey, M. Fink, A. C. Boccara, and S. Gigan, “Controlling light through optical disordered media: transmission matrix approach,” New J. Phys. 13(12), 123021 (2011).
[Crossref]

Bottou, L.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” in Proceedings of the IEEE, vol. 86 (1998), p. 2278.

Brunner, D.

S. Maktoobi, L. Froehly, L. Andreoli, X. Porte, M. Jacquot, L. Larger, and D. Brunner, “Diffractive coupling for photonic networks: how big can we go?” IEEE J. Sel. Top. Quantum Electron. 26(1), 1–8 (2020).
[Crossref]

N. Semenova, X. Porte, L. Andreoli, M. Jacquot, L. Larger, and D. Brunner, “Fundamental aspects of noise in analog- hardware neural networks,” Chaos 29(10), 103128 (2019).
[Crossref]

P. Antonik, N. Marsal, D. Brunner, and D. Rontani, “Human action recognition with a large-scale brain-inspired photonic computer,” Nat. Mach. Intell. 1(11), 530–537 (2019).
[Crossref]

J. Bueno, S. Maktoobi, L. Froehly, I. Fischer, M. Jacquot, L. Larger, and D. Brunner, “Reinforcement learning in a large scale photonic recurrent neural network,” Optica 5(6), 756 (2018).
[Crossref]

G. V. der Sande, D. Brunner, and M. C. Soriano, “Advances in photonic reservoir computing,” Nanophotonics 6(3), 561–576 (2017).
[Crossref]

D. Brunner and I. Fischer, “Reconfigurable semiconductor laser networks based on diffractive coupling,” Opt. Lett. 40(16), 3854 (2015).
[Crossref]

D. Brunner, M. C. Soriano, C. R. Mirasso, and I. Fischer, “Parallel photonic information processing at gigabyte per second data rates using transient states,” Nat. Commun. 4(1), 1364 (2013).
[Crossref]

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. Express 20(3), 3241 (2012).
[Crossref]

P. Antonik, N. Marsal, D. Brunner, and D. Rontani, “Bayesian optimisation of large-scale photonic reservoir computers,” arXiv preprint arXiv:2004.02535 (2020).

Bueno, J.

A. Argyris, J. Bueno, and I. Fischer, “Photonic machine learning implementation for signal recovery in optical communications,” Sci. Rep. 8(1), 8487 (2018).
[Crossref]

J. Bueno, S. Maktoobi, L. Froehly, I. Fischer, M. Jacquot, L. Larger, and D. Brunner, “Reinforcement learning in a large scale photonic recurrent neural network,” Optica 5(6), 756 (2018).
[Crossref]

Buteneers, P.

P. Buteneers, D. Verstraeten, T. W. P. Van Mierlo, D. Stroobandt, R. Raedt, H. Hallez, and B. Schrauwen, “Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing,” Artif. Intell. Medicine 53(3), 215–223 (2011).
[Crossref]

Caputo, B.

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L. Larger, A. Baylón-Fuentes, R. Martinenghi, V. S. Udaltsov, Y. K. Chembo, and M. Jacquot, “High-speed photonic reservoir computing using a time-delay-based architecture: Million words per second classification,” Phys. Rev. X 7(1), 011015 (2017).
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F. Duport, A. Smerieri, A. Akrout, M. Haelterman, and S. Massar, “Fully analogue photonic reservoir computer,” Sci. Rep. 6(1), 22381 (2016).
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J. Dong, M. Rafayelyan, F. Krzakala, and S. Gigan, “Optical reservoir computing using multiple light scattering for chaotic systems prediction,” IEEE J. Sel. Top. Quantum Electron. 26(1), 1–12 (2020).
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K. Lüdge and A. Röhm, “Computing with a camera,” Nat. Mach. Intell. 1(12), 551–552 (2019).
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P. Antonik, N. Marsal, and D. Rontani, “Large-scale spatiotemporal photonic reservoir computer for image classification,” IEEE J. Sel. Top. Quantum Electron. 26(1), 1–12 (2020).
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P. Antonik, N. Marsal, D. Brunner, and D. Rontani, “Human action recognition with a large-scale brain-inspired photonic computer,” Nat. Mach. Intell. 1(11), 530–537 (2019).
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F. D. L. Coarer, M. Sciamanna, A. Katumba, M. Freiberger, J. Dambre, P. Bienstman, and D. Rontani, “All-optical reservoir computing on a photonic chip using silicon-based ring resonators,” IEEE J. Sel. Top. Quantum Electron. 24(6), 1–8 (2018).
[Crossref]

P. Antonik, N. Marsal, D. Brunner, and D. Rontani, “Bayesian optimisation of large-scale photonic reservoir computers,” arXiv preprint arXiv:2004.02535 (2020).

Rybalko, S.

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(24), 244101 (2012).
[Crossref]

Sackesyn, S.

A. Lugnan, A. Katumba, F. Laporte, M. Freiberger, S. Sackesyn, C. Ma, E. Gooskens, J. Dambre, and P. Bienstman, “Photonic neuromorphic information processing and reservoir computing,” APL Photonics 5(2), 020901 (2020).
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Saoud, S.

H. E. Bahi, Z. Mahani, A. Zatni, and S. Saoud, “A robust system for printed and handwritten character recognition of images obtained by camera phone,” in Wseas Trans. signal Process, vol. 11 (2015), pp. 9–22.

Schrauwen, B.

K. Vandoorne, P. Mechet, T. V. Vaerenbergh, M. Fiers, G. Morthier, D. Verstraeten, B. Schrauwen, J. Dambre, and P. Bienstman, “Experimental demonstration of reservoir computing on a silicon photonics chip,” Nat. Commun. 5(1), 3541 (2014).
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Y. Paquot, F. Duport, A. Smerieri, J. Dambre, B. Schrauwen, M. Haelterman, and S. Massar, “Optoelectronic reservoir computing,” Sci. Rep. 2(1), 287 (2012).
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P. Buteneers, D. Verstraeten, T. W. P. Van Mierlo, D. Stroobandt, R. Raedt, H. Hallez, and B. Schrauwen, “Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing,” Artif. Intell. Medicine 53(3), 215–223 (2011).
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L. Appeltant, M. C. Soriano, J. D. G. Van der Sande, S. Massar, J. Dambre, B. Schrauwen, C. R. Mirasso, and I. Fischer, “Information processing using a single dynamical node as complex system,” Nat. Commun. 2(1), 468 (2011).
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E. A. Antonelo, B. Schrauwen, and D. Stroobandt, “Event detection and localization for small mobile robots using reservoir computing,” Neural Networks 21(6), 862–871 (2008).
[Crossref]

D. Verstraeten, B. Schrauwen, M. D’Haene, and D. Stroobandt, “An experimental unification of reservoir computing methods,” Neural Networks 20(3), 391–403 (2007).
[Crossref]

F. Triefenbach, A. Jalal, B. Schrauwen, and J.-P. Martens, “Phoneme recognition with large hierarchical reservoirs,” in Advances in Neural Information Processing Systems, vol. 23 (2010), pp. 2307–2315.

Schuldt, C.

C. Schuldt, I. Laptev, and B. Caputo, “Recognizing human actions: a local svm approach,” in Proceedings of the 17th International Conference on Pattern Recognition, (2004).

Sciamanna, M.

F. D. L. Coarer, M. Sciamanna, A. Katumba, M. Freiberger, J. Dambre, P. Bienstman, and D. Rontani, “All-optical reservoir computing on a photonic chip using silicon-based ring resonators,” IEEE J. Sel. Top. Quantum Electron. 24(6), 1–8 (2018).
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Semenova, N.

N. Semenova, X. Porte, L. Andreoli, M. Jacquot, L. Larger, and D. Brunner, “Fundamental aspects of noise in analog- hardware neural networks,” Chaos 29(10), 103128 (2019).
[Crossref]

Smerieri, A.

F. Duport, A. Smerieri, A. Akrout, M. Haelterman, and S. Massar, “Fully analogue photonic reservoir computer,” Sci. Rep. 6(1), 22381 (2016).
[Crossref]

Q. Vinckier, F. Duport, A. Smerieri, K. Vandoorne, P. Bienstman, M. Haelterman, and S. Massar, “High-performance photonic reservoir computer based on a coherently driven passive cavity,” Optica 2(5), 438–446 (2015).
[Crossref]

Y. Paquot, F. Duport, A. Smerieri, J. Dambre, B. Schrauwen, M. Haelterman, and S. Massar, “Optoelectronic reservoir computing,” Sci. Rep. 2(1), 287 (2012).
[Crossref]

Soriano, M. C.

G. V. der Sande, D. Brunner, and M. C. Soriano, “Advances in photonic reservoir computing,” Nanophotonics 6(3), 561–576 (2017).
[Crossref]

D. Brunner, M. C. Soriano, C. R. Mirasso, and I. Fischer, “Parallel photonic information processing at gigabyte per second data rates using transient states,” Nat. Commun. 4(1), 1364 (2013).
[Crossref]

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. Express 20(3), 3241 (2012).
[Crossref]

L. Appeltant, M. C. Soriano, J. D. G. Van der Sande, S. Massar, J. Dambre, B. Schrauwen, C. R. Mirasso, and I. Fischer, “Information processing using a single dynamical node as complex system,” Nat. Commun. 2(1), 468 (2011).
[Crossref]

Stroobandt, D.

P. Buteneers, D. Verstraeten, T. W. P. Van Mierlo, D. Stroobandt, R. Raedt, H. Hallez, and B. Schrauwen, “Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing,” Artif. Intell. Medicine 53(3), 215–223 (2011).
[Crossref]

E. A. Antonelo, B. Schrauwen, and D. Stroobandt, “Event detection and localization for small mobile robots using reservoir computing,” Neural Networks 21(6), 862–871 (2008).
[Crossref]

D. Verstraeten, B. Schrauwen, M. D’Haene, and D. Stroobandt, “An experimental unification of reservoir computing methods,” Neural Networks 20(3), 391–403 (2007).
[Crossref]

Sugano, C.

Sunada, S.

Takano, K.

Takeda, S.

G. Tanaka, T. Yamane, J.-B. Héroux, R. Nakane, N. Kanazawa, S. Takeda, H. Numata, D. Nakano, and A. Hirose, “Recent advances in physical reservoir computing: A review,” Neural Networks 115, 100–123 (2019).
[Crossref]

Tan, Y.

M. Rafayelyan, J. Dong, Y. Tan, F. Krzakala, and S. Gigan, “Large-scale optical reservoir computing for spatiotemporal chaotic systems prediction,” arXiv:2001.09131v1 (2020).

Tanaka, G.

G. Tanaka, T. Yamane, J.-B. Héroux, R. Nakane, N. Kanazawa, S. Takeda, H. Numata, D. Nakano, and A. Hirose, “Recent advances in physical reservoir computing: A review,” Neural Networks 115, 100–123 (2019).
[Crossref]

Tibshirani, R.

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Springer Series in Statistics (Springer New York Inc., New York, NY, USA, 2001).

Triefenbach, F.

F. Triefenbach, A. Jalal, B. Schrauwen, and J.-P. Martens, “Phoneme recognition with large hierarchical reservoirs,” in Advances in Neural Information Processing Systems, vol. 23 (2010), pp. 2307–2315.

Uchida, A.

Udaltsov, V. S.

L. Larger, A. Baylón-Fuentes, R. Martinenghi, V. S. Udaltsov, Y. K. Chembo, and M. Jacquot, “High-speed photonic reservoir computing using a time-delay-based architecture: Million words per second classification,” Phys. Rev. X 7(1), 011015 (2017).
[Crossref]

Vaerenbergh, T. V.

K. Vandoorne, P. Mechet, T. V. Vaerenbergh, M. Fiers, G. Morthier, D. Verstraeten, B. Schrauwen, J. Dambre, and P. Bienstman, “Experimental demonstration of reservoir computing on a silicon photonics chip,” Nat. Commun. 5(1), 3541 (2014).
[Crossref]

Van der Sande, J. D. G.

L. Appeltant, M. C. Soriano, J. D. G. Van der Sande, S. Massar, J. Dambre, B. Schrauwen, C. R. Mirasso, and I. Fischer, “Information processing using a single dynamical node as complex system,” Nat. Commun. 2(1), 468 (2011).
[Crossref]

Van Mierlo, T. W. P.

P. Buteneers, D. Verstraeten, T. W. P. Van Mierlo, D. Stroobandt, R. Raedt, H. Hallez, and B. Schrauwen, “Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing,” Artif. Intell. Medicine 53(3), 215–223 (2011).
[Crossref]

Vandoorne, K.

Q. Vinckier, F. Duport, A. Smerieri, K. Vandoorne, P. Bienstman, M. Haelterman, and S. Massar, “High-performance photonic reservoir computer based on a coherently driven passive cavity,” Optica 2(5), 438–446 (2015).
[Crossref]

K. Vandoorne, P. Mechet, T. V. Vaerenbergh, M. Fiers, G. Morthier, D. Verstraeten, B. Schrauwen, J. Dambre, and P. Bienstman, “Experimental demonstration of reservoir computing on a silicon photonics chip,” Nat. Commun. 5(1), 3541 (2014).
[Crossref]

Verschaffelt, G.

Verstraeten, D.

K. Vandoorne, P. Mechet, T. V. Vaerenbergh, M. Fiers, G. Morthier, D. Verstraeten, B. Schrauwen, J. Dambre, and P. Bienstman, “Experimental demonstration of reservoir computing on a silicon photonics chip,” Nat. Commun. 5(1), 3541 (2014).
[Crossref]

P. Buteneers, D. Verstraeten, T. W. P. Van Mierlo, D. Stroobandt, R. Raedt, H. Hallez, and B. Schrauwen, “Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing,” Artif. Intell. Medicine 53(3), 215–223 (2011).
[Crossref]

D. Verstraeten, B. Schrauwen, M. D’Haene, and D. Stroobandt, “An experimental unification of reservoir computing methods,” Neural Networks 20(3), 391–403 (2007).
[Crossref]

Vinckier, Q.

Yamane, T.

G. Tanaka, T. Yamane, J.-B. Héroux, R. Nakane, N. Kanazawa, S. Takeda, H. Numata, D. Nakano, and A. Hirose, “Recent advances in physical reservoir computing: A review,” Neural Networks 115, 100–123 (2019).
[Crossref]

Yin, X.

Yoshimura, K.

Zatni, A.

H. E. Bahi, Z. Mahani, A. Zatni, and S. Saoud, “A robust system for printed and handwritten character recognition of images obtained by camera phone,” in Wseas Trans. signal Process, vol. 11 (2015), pp. 9–22.

APL Photonics (1)

A. Lugnan, A. Katumba, F. Laporte, M. Freiberger, S. Sackesyn, C. Ma, E. Gooskens, J. Dambre, and P. Bienstman, “Photonic neuromorphic information processing and reservoir computing,” APL Photonics 5(2), 020901 (2020).
[Crossref]

Artif. Intell. Medicine (1)

P. Buteneers, D. Verstraeten, T. W. P. Van Mierlo, D. Stroobandt, R. Raedt, H. Hallez, and B. Schrauwen, “Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing,” Artif. Intell. Medicine 53(3), 215–223 (2011).
[Crossref]

Chaos (2)

Y. K. Chembo, “Machine learning based on reservoir computing with time-delayed optoelectronic and photonic systems,” Chaos 30(1), 013111 (2020).
[Crossref]

N. Semenova, X. Porte, L. Andreoli, M. Jacquot, L. Larger, and D. Brunner, “Fundamental aspects of noise in analog- hardware neural networks,” Chaos 29(10), 103128 (2019).
[Crossref]

Comput. Sci. Rev. (1)

M. Lukoševičius and H. Jaeger, “Reservoir computing approaches to recurrent neural network training,” Comput. Sci. Rev. 3(3), 127–149 (2009).
[Crossref]

IEEE J. Sel. Top. Quantum Electron. (4)

F. D. L. Coarer, M. Sciamanna, A. Katumba, M. Freiberger, J. Dambre, P. Bienstman, and D. Rontani, “All-optical reservoir computing on a photonic chip using silicon-based ring resonators,” IEEE J. Sel. Top. Quantum Electron. 24(6), 1–8 (2018).
[Crossref]

J. Dong, M. Rafayelyan, F. Krzakala, and S. Gigan, “Optical reservoir computing using multiple light scattering for chaotic systems prediction,” IEEE J. Sel. Top. Quantum Electron. 26(1), 1–12 (2020).
[Crossref]

P. Antonik, N. Marsal, and D. Rontani, “Large-scale spatiotemporal photonic reservoir computer for image classification,” IEEE J. Sel. Top. Quantum Electron. 26(1), 1–12 (2020).
[Crossref]

S. Maktoobi, L. Froehly, L. Andreoli, X. Porte, M. Jacquot, L. Larger, and D. Brunner, “Diffractive coupling for photonic networks: how big can we go?” IEEE J. Sel. Top. Quantum Electron. 26(1), 1–8 (2020).
[Crossref]

J. Lightwave Technol. (1)

Nanophotonics (1)

G. V. der Sande, D. Brunner, and M. C. Soriano, “Advances in photonic reservoir computing,” Nanophotonics 6(3), 561–576 (2017).
[Crossref]

Nat. Commun. (3)

D. Brunner, M. C. Soriano, C. R. Mirasso, and I. Fischer, “Parallel photonic information processing at gigabyte per second data rates using transient states,” Nat. Commun. 4(1), 1364 (2013).
[Crossref]

L. Appeltant, M. C. Soriano, J. D. G. Van der Sande, S. Massar, J. Dambre, B. Schrauwen, C. R. Mirasso, and I. Fischer, “Information processing using a single dynamical node as complex system,” Nat. Commun. 2(1), 468 (2011).
[Crossref]

K. Vandoorne, P. Mechet, T. V. Vaerenbergh, M. Fiers, G. Morthier, D. Verstraeten, B. Schrauwen, J. Dambre, and P. Bienstman, “Experimental demonstration of reservoir computing on a silicon photonics chip,” Nat. Commun. 5(1), 3541 (2014).
[Crossref]

Nat. Mach. Intell. (2)

K. Lüdge and A. Röhm, “Computing with a camera,” Nat. Mach. Intell. 1(12), 551–552 (2019).
[Crossref]

P. Antonik, N. Marsal, D. Brunner, and D. Rontani, “Human action recognition with a large-scale brain-inspired photonic computer,” Nat. Mach. Intell. 1(11), 530–537 (2019).
[Crossref]

Neural Comput. (1)

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(11), 2531–2560 (2002).
[Crossref]

Neural Networks (3)

E. A. Antonelo, B. Schrauwen, and D. Stroobandt, “Event detection and localization for small mobile robots using reservoir computing,” Neural Networks 21(6), 862–871 (2008).
[Crossref]

G. Tanaka, T. Yamane, J.-B. Héroux, R. Nakane, N. Kanazawa, S. Takeda, H. Numata, D. Nakano, and A. Hirose, “Recent advances in physical reservoir computing: A review,” Neural Networks 115, 100–123 (2019).
[Crossref]

D. Verstraeten, B. Schrauwen, M. D’Haene, and D. Stroobandt, “An experimental unification of reservoir computing methods,” Neural Networks 20(3), 391–403 (2007).
[Crossref]

New J. Phys. (1)

S. M. Popoff, G. Lerosey, M. Fink, A. C. Boccara, and S. Gigan, “Controlling light through optical disordered media: transmission matrix approach,” New J. Phys. 13(12), 123021 (2011).
[Crossref]

Opt. Express (3)

Opt. Lett. (2)

Optica (2)

Phys. Rev. Lett. (1)

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(24), 244101 (2012).
[Crossref]

Phys. Rev. X (1)

L. Larger, A. Baylón-Fuentes, R. Martinenghi, V. S. Udaltsov, Y. K. Chembo, and M. Jacquot, “High-speed photonic reservoir computing using a time-delay-based architecture: Million words per second classification,” Phys. Rev. X 7(1), 011015 (2017).
[Crossref]

Sci. Rep. (4)

A. Argyris, J. Bueno, and I. Fischer, “Photonic machine learning implementation for signal recovery in optical communications,” Sci. Rep. 8(1), 8487 (2018).
[Crossref]

S. Sunada and A. Uchida, “Photonic reservoir computing based on nonlinear wave dynamics at microscale,” Sci. Rep. 9(1), 19078 (2019).
[Crossref]

F. Duport, A. Smerieri, A. Akrout, M. Haelterman, and S. Massar, “Fully analogue photonic reservoir computer,” Sci. Rep. 6(1), 22381 (2016).
[Crossref]

Y. Paquot, F. Duport, A. Smerieri, J. Dambre, B. Schrauwen, M. Haelterman, and S. Massar, “Optoelectronic reservoir computing,” Sci. Rep. 2(1), 287 (2012).
[Crossref]

Science (1)

H. Jaeger and H. Haas, “Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication,” Science 304(5667), 78–80 (2004).
[Crossref]

Other (9)

F. Triefenbach, A. Jalal, B. Schrauwen, and J.-P. Martens, “Phoneme recognition with large hierarchical reservoirs,” in Advances in Neural Information Processing Systems, vol. 23 (2010), pp. 2307–2315.

H. Jaeger, “Tech. Rep. GMD Report (German National Research Center for Information Technology),” in Wseas Trans. signal Process, vol. 148 (2001).

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” in Proceedings of the IEEE, vol. 86 (1998), p. 2278.

C. Schuldt, I. Laptev, and B. Caputo, “Recognizing human actions: a local svm approach,” in Proceedings of the 17th International Conference on Pattern Recognition, (2004).

H. E. Bahi, Z. Mahani, A. Zatni, and S. Saoud, “A robust system for printed and handwritten character recognition of images obtained by camera phone,” in Wseas Trans. signal Process, vol. 11 (2015), pp. 9–22.

I. T. Jolliffe, Principal Component Analysis (Springer, 2002), 2nd ed.

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Springer Series in Statistics (Springer New York Inc., New York, NY, USA, 2001).

P. Antonik, N. Marsal, D. Brunner, and D. Rontani, “Bayesian optimisation of large-scale photonic reservoir computers,” arXiv preprint arXiv:2004.02535 (2020).

M. Rafayelyan, J. Dong, Y. Tan, F. Krzakala, and S. Gigan, “Large-scale optical reservoir computing for spatiotemporal chaotic systems prediction,” arXiv:2001.09131v1 (2020).

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