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Photonic neuromorphic information processing and reservoir computing

Alessio Lugnan (UGent) , Andrew Katumba (UGent) , Floris Laporte, Matthias Freiberger (UGent) , Stijn Sackesyn (UGent) , C. Ma, Emmanuel Gooskens (UGent) , Joni Dambre (UGent) and Peter Bienstman (UGent)
(2020) APL PHOTONICS. 5(2).
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Abstract
Photonic neuromorphic computing is attracting tremendous research interest now, catalyzed in no small part by the rise of deep learning in many applications. In this paper, we will review some of the exciting work that has been going in this area and then focus on one particular technology, namely, photonic reservoir computing.
Keywords
TIMING-DEPENDENT PLASTICITY, OPTICAL FEEDBACK, NEURAL-NETWORKS, PERFORMANCE, CLASSIFICATION, IMPLEMENTATION, DYNAMICS, DOMAIN

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Citation

Please use this url to cite or link to this publication:

MLA
Lugnan, Alessio, et al. “Photonic Neuromorphic Information Processing and Reservoir Computing.” APL PHOTONICS, vol. 5, no. 2, 2020.
APA
Lugnan, A., Katumba, A., Laporte, F., Freiberger, M., Sackesyn, S., Ma, C., … Bienstman, P. (2020). Photonic neuromorphic information processing and reservoir computing. APL PHOTONICS, 5(2).
Chicago author-date
Lugnan, Alessio, Andrew Katumba, Floris Laporte, Matthias Freiberger, Stijn Sackesyn, C. Ma, Emmanuel Gooskens, Joni Dambre, and Peter Bienstman. 2020. “Photonic Neuromorphic Information Processing and Reservoir Computing.” APL PHOTONICS 5 (2).
Chicago author-date (all authors)
Lugnan, Alessio, Andrew Katumba, Floris Laporte, Matthias Freiberger, Stijn Sackesyn, C. Ma, Emmanuel Gooskens, Joni Dambre, and Peter Bienstman. 2020. “Photonic Neuromorphic Information Processing and Reservoir Computing.” APL PHOTONICS 5 (2).
Vancouver
1.
Lugnan A, Katumba A, Laporte F, Freiberger M, Sackesyn S, Ma C, et al. Photonic neuromorphic information processing and reservoir computing. APL PHOTONICS. 2020;5(2).
IEEE
[1]
A. Lugnan et al., “Photonic neuromorphic information processing and reservoir computing,” APL PHOTONICS, vol. 5, no. 2, 2020.
@article{8653420,
  abstract     = {{Photonic neuromorphic computing is attracting tremendous research interest now, catalyzed in no small part by the rise of deep learning in many applications. In this paper, we will review some of the exciting work that has been going in this area and then focus on one particular technology, namely, photonic reservoir computing.}},
  articleno    = {{020901}},
  author       = {{Lugnan, Alessio and Katumba, Andrew and Laporte, Floris and Freiberger, Matthias and Sackesyn, Stijn and Ma, C. and Gooskens, Emmanuel and Dambre, Joni and Bienstman, Peter}},
  issn         = {{2378-0967}},
  journal      = {{APL PHOTONICS}},
  keywords     = {{TIMING-DEPENDENT PLASTICITY,OPTICAL FEEDBACK,NEURAL-NETWORKS,PERFORMANCE,CLASSIFICATION,IMPLEMENTATION,DYNAMICS,DOMAIN}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{14}},
  title        = {{Photonic neuromorphic information processing and reservoir computing}},
  url          = {{http://dx.doi.org/10.1063/1.5129762}},
  volume       = {{5}},
  year         = {{2020}},
}
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