Adaptive Optical Dendrites

Livrables

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

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

Report on data management plan

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

Publications

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

Auteurs: Tomas Kulvicius, Minija Tamosiunaite, Florentin Wörgötter
Publié dans: arxiv, 2022
Éditeur: arxiv
DOI: 10.48550/arxiv.2201.11104

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

Auteurs: Rao, A., Legenstein, R., Subramoney, A. and Maass, W.
Publié dans: 2021
Éditeur: biorxiv
DOI: 10.1101/2021.03.04.433822

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

Auteurs: David Kappel; Christian Tetzlaff
Publié dans: biorxiv, Numéro 15, 2022
Éditeur: biorxiv
DOI: 10.1101/2022.04.22.489175

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

Auteurs: Pflüger, M., Ortín, S., Leininger, L., Mirasso, C.R., Argyris, A.
Publié dans: techrxiv, 2024
Éditeur: techrxiv
DOI: 10.36227/techrxiv.170974154.49785806/v2

Microring resonators with external optical feedback for time delay reservoir computing

Auteurs: Giovanni Donati; Claudio R. Mirasso; Mattia Mancinelli; Lorenzo Pavesi; Apostolos Argyris
Publié dans: Optics Express, Numéro 30, 2022, Page(s) 522-537, ISSN 1094-4087
Éditeur: Optical Society of America
DOI: 10.1364/oe.444063

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

Auteurs: Konstantin Möller; David Kappel; Minija Tamosiunaite; Christian Tetzlaff; Bernd Porr; Florentin Wörgötter
Publié dans: PLoS One, Numéro 1, 2022, ISSN 1932-6203
Éditeur: Public Library of Science
DOI: 10.1371/journal.pone.0266679

Dendritic Computing: Branching Deeper into Machine Learning

Auteurs: Acharya, J., Basu, A., Legenstein, R., Limbacher, T., Poirazi, P., and Wu, X.
Publié dans: Neuroscience, 2021, ISSN 0306-4522
Éditeur: Elsevier BV
DOI: 10.1016/j.neuroscience.2021.10.001

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

Auteurs: Ortín González, Silvia; Soriano, Miguel C.; Fischer, Ingo; Mirasso, Claudio R.; Argyris, Apostolos
Publié dans: Optical Materials Express, Numéro 12, 2022, Page(s) 1907-1919, ISSN 2159-3930
Éditeur: Optical Society of America
DOI: 10.1364/ome.453506

Bootstrapping Concept Formation in Small Neural Networks

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

Unsupervised learning of perceptual feature combinations

Auteurs: Tamosiunaite, Minija; Tetzlaff, Christian; Wörgötter, Florentin; Bush, Daniel
Publié dans: PLoS Computational Biology, Numéro 20(3), 2024, ISSN 1553-734X
Éditeur: Public Library of Science
DOI: 10.1371/journal.pcbi.1011926

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

Auteurs: Maximilian Baronig; Robert Legenstein
Publié dans: Frontiers in Neuroscience, Vol 17 (2024), Numéro 9, 2024, ISSN 1662-453X
Éditeur: Frontiers
DOI: 10.3389/fnins.2023.1276706

Implementation of input correlation learning with an optoelectronic dendritic unit

Auteurs: Silvia Ortín; Miguel C. Soriano; Christian Tetzlaff; Florentin Wörgötter; Ingo Fischer; Claudio R. Mirasso; Apostolos Argyris
Publié dans: Frontiers in Physics, Numéro 11, 2023, Page(s) 1112295, ISSN 2296-424X
Éditeur: Frontiers Media
DOI: 10.3389/fphy.2023.1112295

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

Auteurs: Susi, G., Antón-Toro, L.F., Maestú, F., Pereda, E. and Mirasso, C.
Publié dans: Frontiers in Neuroscience, 2021, ISSN 1662-4548
Éditeur: 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

Auteurs: Mirko Goldmann, Claudio R. Mirasso, Ingo Fischer, and Miguel C. Soriano
Publié dans: Physical Review E, Numéro 106, 2022, Page(s) 044211, ISSN 1539-3755
Éditeur: American Physical Society
DOI: 10.1103/physreve.106.044211

Unveiling the role of plasticity rules in reservoir computing

Auteurs: Morales, G.B., Mirasso, C.R. and Soriano, M.C.
Publié dans: Neurocomputing, Numéro 461, 2021, Page(s) 705-715, ISSN 0925-2312
Éditeur: Elsevier BV
DOI: 10.1016/j.neucom.2020.05.127

Photonic neuromorphic technologies in optical communications

Auteurs: Argyris, Apostolos
Publié dans: Nanophotonics, Numéro 11, 2022, ISSN 2192-8614
Éditeur: Walter de Gruyter
DOI: 10.1515/nanoph-2021-0578

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