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H2020: Neoteric

Full Name: Holistic photonic ML paradigms for most challenging imaging applications

Duration: 1/3/2020-1/3/2022


  • Universitat Politecnica de Valencia
  • CERTHCentre for Research & Technology
  • Teramount
  • IBM
  • UGhent
  • University of the Aegean


  • NEoteRICss primary objective is the generation of holistic photonic machine learning paradigms that will address demanding imaging applications in an unconventional approach providing paramount frame rate increase, classification performance enhancement and orders of magnitude lower power consumption compared to the state-of-the-art machine learning approaches.
  • NEoteRICss implementation stratagem incorporates multiple innovations spanning from the photonic transistor level and extending up to the system architectural level, thus paving new, unconventional routes to neuromorphic performance enhancement.

INTEC's Role:

  • Neuromorphic architectures, training approaches and benchmarking.

Project Web site:

People involved

Publications in the framework of this project (14)

    International Journals

  1. S. Masaad, P. Bienstman, Opto-Electronic Machine Learning Network for Kramers-Kronig Receiver Linearization , Optics Express, (2024)  Download this Publication (2.6MB).
  2. E.J.C. Gooskens, S. Sackesyn, J. Dambre, P. Bienstman, Experimental results on nonlinear distortion compensation using photonic reservoir computing with a single set of weights for different wavelengths, Scientific Reports, 13(21399), (2023)  Download this Publication (1.4MB).
  3. M. Gouda, A. Lugnan, J. Dambre, G. V. Branden, C. Posch, P. Bienstman, Improving the classification accuracy in label-free flow cytometry using event-based vision and simple logistic regression, IEEE Journal on Selected Topics in Quantum Electronics, (Optical computing), p.8 doi:10.1109/JSTQE.2023.3244040 (2023)  Download this Publication (4.8MB).
  4. S. Masaad, E.J.C. Gooskens, S. Sackesyn, J. Dambre, P. Bienstman, Photonic reservoir computing for nonlinear equalization of 64-QAM signals with a Kramers-Kronig receiver, Nanophotonics, (2022)  Download this Publication (1.7MB).
  5. E.J.C. Gooskens, F. Laporte, C. Ma, S. Sackesyn, P. Bienstman, Wavelength Dimension in Waveguide-Based Photonic Reservoir Computing, Optics Express, 30(9), p.15634-15647 doi:10.1364/OE.455774 (2022).
  6. A. Lugnan, E.J.C. Gooskens, J. Vatin, J. Dambre, P. Bienstman, Machine learning issues and opportunities in ultrafast particle classification for label‑free microflow cytometry, Scientific Reports, 10(1), p.1-13 doi:10.1038/s41598-020-77765-w (2020)  Download this Publication (2MB).
      International Conferences

    1. M. Gouda, Steven Abreu, P. Bienstman, Training Strategies for Spiking Neural Networks Integrated with Event-Based Vision in Label-Free Flow Cytometry, Annual Symposium of the IEEE Photonics Society Benelux Chapter, Belgium, (2023).
    2. P. Bienstman, A. Lugnan, S. Aggarwal, F. Brückerhoff-Plückelmann, W. Pernice, H. Bhaskaran, C. Ma, S. Sackesyn, E.J.C. Gooskens, S. Masaad, M. Gouda, R. De Prins, Optical computing in silicon photonics: self-adapting ring networks and quantum recurrent neural networks, Natural and Physical Computing (NNPC), Germany, p.1 (2023)  Download this Publication (207KB).
    3. Steven Abreu, M. Gouda, A. Lugnan, P. Bienstman, Flow cytometry with event-based vision and spiking neuromorphic hardware, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Canada, doi:10.1109/CVPRW59228.2023.00435 (2023)  Download this Publication (2.7MB).
    4. E.J.C. Gooskens, S. Sackesyn, S. Masaad, J. Dambre, P. Bienstman, Photonic Reservoir Computing for Wavelength Multiplexed Nonlinear Fiber Distortion Mitigation, IEEE SiPhotonics (formerly GFP conference), United States, p.1-2 doi:10.1109/SiPhotonics55903.2023.10141896 (2023)  Download this Publication (1MB).
    5. M. Gouda, A. Lugnan, J. Dambre, G. V. Branden, C. Posch, P. Bienstman, Event-based vision for improved classification accuracy in label-free flow cytometry, IEEE Benelux Photonics Chapter - Annual Symposium 2022, Netherlands, p.26-29 (2022)  Download this Publication (2.2MB).
    6. S. Masaad, E.J.C. Gooskens, S. Sackesyn, J. Dambre, P. Bienstman, Photonic Reservoir Computing for Nonlinear Equalization of 64-QAM Signals with a Kramers-Kronig Receiver, European Conference on Optical Communication, Tu4G.3 , Switzerland, (2022)  Download this Publication (352KB).
    7. E.J.C. Gooskens, F. Laporte, S. Sackesyn, C. Ma, P. Bienstman, Wavelength Multiplexing in Photonic Reservoir Computing, Annual Symposium of the IEEE Photonics Society Benelux Chapter, (2021)  Download this Publication (546KB).
    8. A. Lugnan, S. Sackesyn, C. Ma, E.J.C. Gooskens, M. Gouda, S. Masaad, Joni Dambre, P. Bienstman, Photonic reservoir computing for high-speed neuromorphic computing applications, 2021 IEEE Summer Topicals Meeting Series (invited), Mexico, (2021).