Adaptive Optical Dendrites

Informazioni relative al progetto

ADOPD

ID dell’accordo di sovvenzione: 899265

DOI

10.3030/899265

Progetto chiuso

Data della firma CE 26 Maggio 2020

Data di avvio 1 Ottobre 2020

Data di completamento 31 Marzo 2024

Finanziato da

• EXCELLENT SCIENCE - Future and Emerging Technologies (FET)

Costo totale

€ 2 879 375,00

Contributo UE

€ 2 818 875,00

Coordinato da

GEORG-AUGUST-UNIVERSITAT GOTTINGEN STIFTUNG OFFENTLICHEN RECHTS
Germany

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Documents, reports (10)

Update data management plan
Report on dendritic functionalities allowing adaptive dendritic computation using multi-branched dendrites

This deliverable will report on how to include adaptation (synaptic plasticity) into the multiple branched dendritic models for use on optic fibers.

Report on principles of context-dependent computations with networks of dendritic units

"This deliverable will report on how to include ""context"" into the computations that can be performed with networks of dendritic units. Context can render an important piece of information for solving complex spatio-temporal problems and larger networks of ODUs should be able to handle this, too."

Report on the definition of KPIs

This deliverable deals with the definition and description of possible Key Performance Indices KPIs for ADOPD It it foreseen to critically rereview the usefulness of the heredefined KPIs at month 18 in the Periodic Report and possibly amend them following the needs of the project

Update Dissemination and Expoitation Plan

Update of Dissemination and Explotation Plan

Report on gradient-based optimization techniques for networks of dendritic units

This is the first report where we will address larger networks of dendritic units Here we will address the question how to use different optimization methods gradientbased for achieving convergence when faced with different tasks

Report on dendritic functionalities allowing adaptive dendritic computation using single-branched dendrites

This deliverable will report on how to include adaptation synaptic plasticity into the single branch dendritic models for use on optic fibers

Benchmarking Report

This is the report on system performance in view of the KPIs defined in deliverable D5.3 (and potentially revised in the periodic report at month 18).

Report on first non-linear model of transfer to optical systems

This deliverable will report on how to transfer a nonlinear synapticdendritic model to the optical fiber systems single mode fibers

Dissemination and Explotation plan

Dissemination and Exploitation Plan

Websites, patent fillings, videos etc. (3)

Project Flyer

This deliverable will be a flyer for dissemination to potentially interested parties in science and industry

Internal Workshop

Towards the end of the project we will organize a targeted workshop with a selected group of external participants. We plant to use a small part of the budget from every partner to cover the costs for this WS.

Project Web site

Project Web site with Logo up and running Contains public and private area

Open Research Data Pilot (1)

Report on data management plan

This deliverable will report on the data management plan of ADOPD according the OpenResearch Data Pilot

Pubblicazioni

Non-peer reviewed articles (1)

Combining optimal path search with task-dependent learning in a neural network

Autori: Tomas Kulvicius, Minija Tamosiunaite, Florentin Wörgötter
Pubblicato in: IEEE Transactions on Neural Networks and Learning Systems, 2023, ISSN 2162-237X
Editore: IEEE Computational Intelligence Society
DOI: 10.1109/tnnls.2023.3327103

Peer reviewed articles (12)

Microring resonators with external optical feedback for time delay reservoir computing

Autori: Giovanni Donati; Claudio R. Mirasso; Mattia Mancinelli; Lorenzo Pavesi; Apostolos Argyris
Pubblicato in: Optics Express, Numero 30, 2022, Pagina/e 522-537, ISSN 1094-4087
Editore: Optical Society of America
DOI: 10.1364/oe.444063

Differential Hebbian learning with time-continuous signals for active noise reduction

Autori: Konstantin Möller; David Kappel; Minija Tamosiunaite; Christian Tetzlaff; Bernd Porr; Florentin Wörgötter
Pubblicato in: PLoS One, Numero 1, 2022, ISSN 1932-6203
Editore: Public Library of Science
DOI: 10.1371/journal.pone.0266679

Dendritic Computing: Branching Deeper into Machine Learning

Autori: Acharya, J., Basu, A., Legenstein, R., Limbacher, T., Poirazi, P., and Wu, X.
Pubblicato in: Neuroscience, 2021, ISSN 0306-4522
Editore: Elsevier BV
DOI: 10.1016/j.neuroscience.2021.10.001

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

Autori: Ortín González, Silvia; Soriano, Miguel C.; Fischer, Ingo; Mirasso, Claudio R.; Argyris, Apostolos
Pubblicato in: Optical Materials Express, Numero 12, 2022, Pagina/e 1907-1919, ISSN 2159-3930
Editore: Optical Society of America
DOI: 10.1364/ome.453506

Bootstrapping Concept Formation in Small Neural Networks

Autori: Tamosiunaite, M., Kulvicius, T., Wörgötter, F.
Pubblicato in: IEEE Transactions on Cognitive and Developmental Systems, 2021, ISSN 2379-8920
Editore: Institute of Electrical and Electronics Engineers Inc.
DOI: 10.1109/tcds.2022.3163022

Unsupervised learning of perceptual feature combinations

Autori: Tamosiunaite, Minija; Tetzlaff, Christian; Wörgötter, Florentin; Bush, Daniel
Pubblicato in: PLoS Computational Biology, Numero 20(3), 2024, ISSN 1553-734X
Editore: Public Library of Science
DOI: 10.1371/journal.pcbi.1011926

Context association in pyramidal neurons through local synaptic plasticity in apical dendrites

Autori: Maximilian Baronig; Robert Legenstein
Pubblicato in: Frontiers in Neuroscience, Vol 17 (2024), Numero 9, 2024, ISSN 1662-453X
Editore: Frontiers
DOI: 10.3389/fnins.2023.1276706

Implementation of input correlation learning with an optoelectronic dendritic unit

Autori: Silvia Ortín; Miguel C. Soriano; Christian Tetzlaff; Florentin Wörgötter; Ingo Fischer; Claudio R. Mirasso; Apostolos Argyris
Pubblicato in: Frontiers in Physics, Numero 11, 2023, Pagina/e 1112295, ISSN 2296-424X
Editore: Frontiers Media
DOI: 10.3389/fphy.2023.1112295

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

Autori: Susi, G., Antón-Toro, L.F., Maestú, F., Pereda, E. and Mirasso, C.
Pubblicato in: Frontiers in Neuroscience, 2021, ISSN 1662-4548
Editore: Frontiers Research Foundation
DOI: 10.3389/fnins.2021.582608

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

Autori: Mirko Goldmann, Claudio R. Mirasso, Ingo Fischer, and Miguel C. Soriano
Pubblicato in: Physical Review E, Numero 106, 2022, Pagina/e 044211, ISSN 1539-3755
Editore: American Physical Society
DOI: 10.1103/physreve.106.044211

Unveiling the role of plasticity rules in reservoir computing

Autori: Morales, G.B., Mirasso, C.R. and Soriano, M.C.
Pubblicato in: Neurocomputing, Numero 461, 2021, Pagina/e 705-715, ISSN 0925-2312
Editore: Elsevier BV
DOI: 10.1016/j.neucom.2020.05.127

Photonic neuromorphic technologies in optical communications

Autori: Argyris, Apostolos
Pubblicato in: Nanophotonics, Numero 11, 2022, ISSN 2192-8614
Editore: Walter de Gruyter
DOI: 10.1515/nanoph-2021-0578

Other (3)

A normative framework for learning top-down predictions through synaptic plasticity in apical dendrites

Autori: Rao, A., Legenstein, R., Subramoney, A. and Maass, W.
Pubblicato in: 2021
Editore: biorxiv
DOI: 10.1101/2021.03.04.433822

Synapses learn to utilize pre-synaptic noise for the prediction of postsynaptic dynamics

Autori: David Kappel; Christian Tetzlaff
Pubblicato in: biorxiv, Numero 15, 2022
Editore: biorxiv
DOI: 10.1101/2022.04.22.489175

Experimental multi-bit header classification at 28.5 Gb/s with step-index few-mode fibers

Autori: Pflüger, M., Ortín, S., Leininger, L., Mirasso, C.R., Argyris, A.
Pubblicato in: techrxiv, 2024
Editore: techrxiv
DOI: 10.36227/techrxiv.170974154.49785806/v2