H. Jaeger and H. Haas, Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication, Science, vol.304, pp.78-80, 2004.

W. Maass, T. Natschläger, and H. Markram, Real-time computing without stable states: A new framework for neural computation based on perturbations, Neural Comput, vol.14, pp.2531-2560, 2002.

K. Vandoorne, P. Mechet, T. Van-vaerenbergh, M. Fiers, G. Morthier et al., Experimental demonstration of reservoir computing on a silicon photonics chip, Nat. Commun, vol.5, p.3541, 2014.

L. Larger, A. Baylón-fuentes, R. Martinenghi, V. S. Udaltsov, Y. K. Chembo et al., High-speed photonic reservoir computing using a time-delay-based architecture: Million words per second classification, Phys. Rev. X, vol.7, p.11015, 2017.

J. Bueno, S. Maktoobi, L. Froehly, I. Fischer, M. Jacquot et al.,

. Brunner, Reinforcement learning in a large-scale photonic recurrent neural network, Optica, vol.5, p.756, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02134484

F. D. Coarer, M. Sciamanna, A. Katumba, M. Freiberger, J. Dambre et al.,

D. Bienstman and . Rontani, All-optical reservoir computing on a photonic chip using silicon-based ring resonators, IEEE J. Sel. Top. Quantum Electron, vol.24, pp.1-8, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01810711

P. Antonik, N. Marsal, and D. Rontani, Large-scale spatiotemporal photonic reservoir computer for image classification, IEEE J. Sel. Top. Quantum Electron, vol.26, pp.1-12, 2020.
URL : https://hal.archives-ouvertes.fr/hal-02403762

L. Appeltant, M. C. Soriano, G. Van-der-sande, J. Danckaert, S. Massar et al., Information processing using a single dynamical node as complex system, Nat. Commun, vol.2, pp.466-468, 2011.

Y. Paquot, F. Duport, A. Smerieri, J. Dambre, B. Schrauwen et al., Optoelectronic reservoir computing, Sci. Rep, vol.2, p.287, 2012.

P. Antonik, M. Haelterman, and S. Massar, Online training for highperformance analogue readout layers in photonic reservoir computers, Cognit. Comput, vol.9, pp.297-306, 2017.

K. Takano, C. Sugano, M. Inubushi, K. Yoshimura, S. Sunada et al., Compact reservoir computing with a photonic integrated circuit, Opt. Express, vol.26, pp.29424-29439, 2018.

F. Duport, B. Schneider, A. Smerieri, M. Haelterman, and S. Massar, All-optical reservoir computing, Opt. Express, vol.20, p.22783, 2012.

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, vol.4, pp.1364-1367, 2013.

K. Hicke, M. A. Escalona-moran, D. Brunner, M. C. Soriano, I. Fischer et al., Information processing using transient dynamics of semiconductor lasers subject to delayed feedback, IEEE J. Sel. Top. Quantum Electron, vol.19, p.1501610, 2013.

R. M. Nguimdo, G. Verschaffelt, J. Danckaert, and G. Van-der-sande, Simultaneous computation of two independent tasks using reservoir computing based on a single photonic nonlinear node with optical feedback, IEEE Trans. Neural Networks Learn. Syst, vol.26, pp.3301-3307, 2015.

A. Argyris, J. Bueno, and I. Fischer, Photonic machine learning implementation for signal recovery in optical communications, Sci. Rep, vol.8, p.8487, 2017.

K. Harkhoe and G. V. Sande, Delay-based reservoir computing using multimode semiconductor lasers: Exploiting the rich carrier dynamics, IEEE J. Sel. Top. Quantum Electron, vol.25, p.1, 2019.

M. Müller, W. Hofmann, T. Gründl, M. Horn, P. Wolf et al., 1550-nm highspeed short-cavity VCSELs, IEEE J. Sel. Top. Quantum Electron, vol.17, pp.1158-1166, 2011.

J. Vatin, D. Rontani, and M. Sciamanna, Enhanced performance of a reservoir computer using polarization dynamics in VCSELs, Opt. Lett, vol.43, p.4497, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01877236

J. Vatin, D. Rontani, and M. Sciamanna, Experimental reservoir computing using VCSEL polarization dynamics, Opt. Express, vol.27, p.18579, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02169548

X. Guo, S. Xiang, Y. Zhang, L. Lin, A. Wen et al., Polarization multiplexing reservoir computing based on a VCSEL with polarized optical feedback, IEEE J. Sel. Top. Quantum Electron, vol.26, p.1, 2019.

R. Essiambre, G. Kramer, P. J. Winzer, G. J. Foschini, and B. Goebel, Capacity limits of optical fiber networks, J. Lightwave Technol, vol.28, pp.662-701, 2010.

R. Lang and K. Kobayashi, External optical feedback effects on semiconductor injection laser properties, IEEE J. Quantum Electron, vol.16, pp.347-355, 1980.

G. P. , Nonlinear fiber optics, pp.195-211, 2000.

K. Hammani, B. Kibler, C. Finot, P. Morin, J. Fatome et al., Peregrine soliton generation and breakup in standard telecommunications fiber, Opt. Lett, vol.36, p.112, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00542997

A. Argyris, J. Bueno, and I. Fischer, PAM-4 transmission at 1550 nm using photonic reservoir computing post-processing, IEEE Access, vol.7, pp.37017-37025, 2019.

M. Sciamanna, K. Panajotov, H. Thienpont, I. Veretennicoff, P. Mégret et al., Optical feedback induces polarization mode hopping in verticalcavity surface-emitting lasers, Opt. Lett, vol.28, pp.1543-1545, 2003.

P. J. Winzer, D. T. Neilson, and A. R. Chraplyvy, Fiber-optic transmission and networking: The previous 20 and the next 20 years, Opt. Express, vol.26, p.24190, 2018.

N. Eiselt, J. Wei, H. Griesser, A. Dochhan, M. H. Eiselt et al., Evaluation of real-time 8 × 56.25 Gb/s (400 G) PAM-4 for inter-data center application over 80 km of SSMF at 1550 nm, J. Lightwave Technol, vol.35, pp.955-962, 2017.

D. Zibar, M. Piels, R. Jones, and C. G. Schäeffer, Machine learning techniques in optical communication, J. Lightwave Technol, vol.34, pp.1442-1452, 2016.