Simulating self-learning in photorefractive optical reservoir computers

Simulating self-learning in photorefractive optical reservoir computers

Abstract Photorefractive materials exhibit an interesting plasticity under the influence of an optical field. By extending the finite-difference time-domain method to include the photorefractive effect, we explore how this property can be exploited in the context of neuromorphic computing for teleco...

Description complète

Enregistré dans:
Détails bibliographiques
Auteurs principaux: Floris Laporte, Joni Dambre, Peter Bienstman
Format: article
Langue:EN
Publié: Nature Portfolio 2021
Sujets:
Medicine
R
Science
Q
Accès en ligne:https://doaj.org/article/29b2a9f56c7c4082a9533fd4bbd1c787
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
id oai:doaj.org-article:29b2a9f56c7c4082a9533fd4bbd1c787
record_format dspace
spelling oai:doaj.org-article:29b2a9f56c7c4082a9533fd4bbd1c7872021-12-02T13:57:48ZSimulating self-learning in photorefractive optical reservoir computers10.1038/s41598-021-81899-w2045-2322https://doaj.org/article/29b2a9f56c7c4082a9533fd4bbd1c7872021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81899-whttps://doaj.org/toc/2045-2322Abstract Photorefractive materials exhibit an interesting plasticity under the influence of an optical field. By extending the finite-difference time-domain method to include the photorefractive effect, we explore how this property can be exploited in the context of neuromorphic computing for telecom applications. By first priming the photorefractive material with a random bit stream, the material reorganizes itself to better recognize simple patterns in the stream. We demonstrate this by simulating a typical reservoir computing setup, which gets a significant performance boost on performing the XOR on two consecutive bits in the stream after this initial priming step.Floris LaporteJoni DambrePeter BienstmanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Floris Laporte
Joni Dambre
Peter Bienstman
Simulating self-learning in photorefractive optical reservoir computers
description Abstract Photorefractive materials exhibit an interesting plasticity under the influence of an optical field. By extending the finite-difference time-domain method to include the photorefractive effect, we explore how this property can be exploited in the context of neuromorphic computing for telecom applications. By first priming the photorefractive material with a random bit stream, the material reorganizes itself to better recognize simple patterns in the stream. We demonstrate this by simulating a typical reservoir computing setup, which gets a significant performance boost on performing the XOR on two consecutive bits in the stream after this initial priming step.
format article
author Floris Laporte
Joni Dambre
Peter Bienstman
author_facet Floris Laporte
Joni Dambre
Peter Bienstman
author_sort Floris Laporte
title Simulating self-learning in photorefractive optical reservoir computers
title_short Simulating self-learning in photorefractive optical reservoir computers
title_full Simulating self-learning in photorefractive optical reservoir computers
title_fullStr Simulating self-learning in photorefractive optical reservoir computers
title_full_unstemmed Simulating self-learning in photorefractive optical reservoir computers
title_sort simulating self-learning in photorefractive optical reservoir computers
publisher Nature Portfolio
publishDate 2021
url https://doaj.org/article/29b2a9f56c7c4082a9533fd4bbd1c787
work_keys_str_mv AT florislaporte simulatingselflearninginphotorefractiveopticalreservoircomputers
AT jonidambre simulatingselflearninginphotorefractiveopticalreservoircomputers
AT peterbienstman simulatingselflearninginphotorefractiveopticalreservoircomputers
_version_ 1718392249870450688

Documents similaires