Adaptive Optical Dendrites

Leistungen

Update data management plan
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

Report on the numerical results of SMF-based ODU topologies.

This deliverable will report on how optical dendritic units ODUs will behave when implemented on single mode fibers SMF It will deal with numerical theoretical investigations of this

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

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

Project Flyer

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

Project Web site

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

Report on data management plan

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

Veröffentlichungen

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

Autoren: Tomas Kulvicius, Minija Tamosiunaite, Florentin Wörgötter
Veröffentlicht in: arxiv, 2022
Herausgeber: arxiv
DOI: 10.48550/arxiv.2201.11104

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

Autoren: Rao, A., Legenstein, R., Subramoney, A. and Maass, W.
Veröffentlicht in: 2021
Herausgeber: biorxiv
DOI: 10.1101/2021.03.04.433822

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

Autoren: David Kappel; Christian Tetzlaff
Veröffentlicht in: biorxiv, Ausgabe 15, 2022
Herausgeber: biorxiv
DOI: 10.1101/2022.04.22.489175

Microring resonators with external optical feedback for time delay reservoir computing

Autoren: Giovanni Donati; Claudio R. Mirasso; Mattia Mancinelli; Lorenzo Pavesi; Apostolos Argyris
Veröffentlicht in: Optics Express, Ausgabe 30, 2022, Seite(n) 522-537, ISSN 1094-4087
Herausgeber: Optical Society of America
DOI: 10.1364/oe.444063

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

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

Dendritic Computing: Branching Deeper into Machine Learning

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

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

Autoren: Ortín González, Silvia; Soriano, Miguel C.; Fischer, Ingo; Mirasso, Claudio R.; Argyris, Apostolos
Veröffentlicht in: Optical Materials Express, Ausgabe 12, 2022, Seite(n) 1907-1919, ISSN 2159-3930
Herausgeber: Optical Society of America
DOI: 10.1364/ome.453506

Bootstrapping Concept Formation in Small Neural Networks

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

Unsupervised learning of perceptual feature combinations

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

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

Autoren: Maximilian Baronig; Robert Legenstein
Veröffentlicht in: Frontiers in Neuroscience, Vol 17 (2024), Ausgabe 9, 2024, ISSN 1662-453X
Herausgeber: Frontiers
DOI: 10.3389/fnins.2023.1276706

Implementation of input correlation learning with an optoelectronic dendritic unit

Autoren: Silvia Ortín; Miguel C. Soriano; Christian Tetzlaff; Florentin Wörgötter; Ingo Fischer; Claudio R. Mirasso; Apostolos Argyris
Veröffentlicht in: Frontiers in Physics, Ausgabe 11, 2023, Seite(n) 1112295, ISSN 2296-424X
Herausgeber: Frontiers Media
DOI: 10.3389/fphy.2023.1112295

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

Autoren: Susi, G., Antón-Toro, L.F., Maestú, F., Pereda, E. and Mirasso, C.
Veröffentlicht in: Frontiers in Neuroscience, 2021, ISSN 1662-4548
Herausgeber: 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

Autoren: Mirko Goldmann, Claudio R. Mirasso, Ingo Fischer, and Miguel C. Soriano
Veröffentlicht in: Physical Review E, Ausgabe 106, 2022, Seite(n) 044211, ISSN 1539-3755
Herausgeber: American Physical Society
DOI: 10.1103/physreve.106.044211

Unveiling the role of plasticity rules in reservoir computing

Autoren: Morales, G.B., Mirasso, C.R. and Soriano, M.C.
Veröffentlicht in: Neurocomputing, Ausgabe 461, 2021, Seite(n) 705-715, ISSN 0925-2312
Herausgeber: Elsevier BV
DOI: 10.1016/j.neucom.2020.05.127

Photonic neuromorphic technologies in optical communications

Autoren: Argyris, Apostolos
Veröffentlicht in: Nanophotonics, Ausgabe 11, 2022, ISSN 2192-8614
Herausgeber: Walter de Gruyter
DOI: 10.1515/nanoph-2021-0578

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