POST-DIGITAL
POST-DIGITAL POST-DIGITAL: NEUROMORPHIC COMPUTING IN PHOTONIC AND OTHER NONLINEAR MEDIA

  • I.P.: Ingo Fischer, Claudio Mirasso
  • Partners: CSIC, UBFC, U Groningen, ULB, IBM, LightOn, VLC Photonics, IMEC, Thales
  • Página web: https://postdigital.astonphotonics.uk
  • Fecha de inicio: 1 de Abril de 2020
  • Fecha de final: 31 de Marzo de 2024

POST-DIGITAL is a Marie Skłodowska-Curie Innovative Training Network, funded by the European Union’s Horizon 2020 research and innovation programme.
POST-DIGITAL is committed to form a new generation of engineers and researchers, affording them with a uniquely broad interdisciplinary education and training which enables them to design and develop unconventional computing systems which, for the first time, pass over the threshold of real-world exploitability and which thereby help Europe’s ICT landscape to master the transformation out of the impending crises of digital technologies.
POST-DIGITAL brings together fourteen leading academic and industrial players (including IBM, Thales and three highly reputed SMEs) in optical and neuromorphic computing, already connected in long-standing collaboration networks. These groups will now join forces in order to train through ground breaking research, complimentary skills training and industrial secondments fifteen high calibre early-stage researchers to become backbone innovators in a technology domain that is crucial for Europe’s sustained competitiveness in ICT scenarios facing a dramatic pressure for fundamental re-thinking.

Investigadores

  • Apostolos Argyris

    Apostolos Argyris

  • Miguel C. Soriano

    Miguel C. Soriano

  • Ingo Fischer

    Ingo Fischer

  • Claudio Mirasso

    Claudio Mirasso

  • Lucas Talandier

    Lucas Talandier

  • Mirko Goldmann

    Mirko Goldmann

Publicaciones recientes

Exploiting oscillatory dynamics of delay systems for reservoir computing

Goldmann, Mirko; Fischer, Ingo ; Mirasso, Claudio R.; Soriano, Miguel C.
Chaos 33, 093139 (2023)

Integrated programmable spectral filter for frequency-multiplexed neuromorphic computers

Jonuzi, Tigers; Lupo, Alessandro; Soriano, Miguel C.; Massar, Serge; Doménech, J. D.
Optics Express 31, 19255-19265 (2023)

Neural network learning with photonics and for photonic circuit design

Brunner, D.; Soriano, M. C.; Fan, S.
Nanophotonics 12, 773-775 (2023)

Learn one size to infer all: Exploiting translational symmetries in delay-dynamical and spatiotemporal systems using scalable neural networks

Goldmann, Mirko; Mirasso, Claudio R.; Fischer, Ingo; Soriano, Miguel C.
Physical Review E 106, 044211 (2022)

Information Processing Capacity of a Single-Node Reservoir Computer: An Experimental Evaluation

Vettelschoss, Benedikt; Röhm, André; Soriano, Miguel C.
IEEE Transactions on Neural Networks and Learning Systems 33, 2714-2725 (2022)

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