• I.P.: Ingo Fischer, Claudio Mirasso
  • Partners: University of Göttingen, CSIC (IFISC), UIB (IFISC), University of Graz, LEONI Fiber Optics
  • Pàgina web:
  • Data d'inici: 1 de octubre de 2020
  • Data de finalització: 31 de març de 2024

The increased demand for computation with low energy consumption requires entirely novel hardware concepts. In ADOPD we develop ultra-fast computational units based on optical-fiber technologies exploiting information processing principles used by neurons in their dendritic trees. Dendritic processing is highly condensed, local, and parallel and it allows also for non-linear computations. These properties will first be modelled and in a second step transferred to optical systems consisting of fiber optics as well as other optical components. For the first prototype, ADOPD uses well-established single-mode fiber technology to build an optical-dendritic unit (ODU). From there, we move on to cutting-edge multi-mode fibers to obtain an all-optical second prototype of a dendritic tree with significantly higher computing power and compactness. Finally we will design computational models of networks of multiple ODUs to quantify the computational efficiency such multiple, parallel operating devices. Thus, the optical dendritic units created by ADOPD represent a novel, cutting-edge computing hardware for fast, low-power, parallel computing, with the potential to help addressing the rising demands for computation.


  • Apostolos Argyris

    Apostolos Argyris

  • Miguel C. Soriano

    Miguel C. Soriano

  • Ingo Fischer

    Ingo Fischer

  • Claudio Mirasso

    Claudio Mirasso

  • Irene Estébanez

  • Jaime Sánchez

    Jaime Sánchez

  • André Röhm

    André Röhm

Publicacions recents

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)

Optical dendrites for spatio-temporal computing with few-mode fibers

Ortín, Silvia; Soriano, Miguel C.; Fischer, Ingo; Mirasso, Claudio R.; Argyris, Apostolos
Optical Materials Express 12 (5), 1907-1919 (2022)

Unveiling the role of plasticity rules in reservoir computing

Morales, Guillermo B.; Mirasso, Claudio R.; Soriano, Miguel C.
Neurocomputing 461, 705-715 (2021)

nMNSD—A Spiking Neuron-Based Classifier That Combines Weight-Adjustment and Delay-Shift

Susi, G.; Antón-Toro, L. F.; Maestú, F.; Pereda, E.; Mirasso, C.
Frontiers in Neuroscience 15, Article 582608 (2021)

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