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Regenerative memory in time-delayed neuromorphic photonic resonators

We investigate a photonic regenerative memory based upon a neuromorphic oscillator with a delayed self-feedback (autaptic) connection. We disclose the existence of a unique temporal response characteristic of localized structures enabling an ideal support for bits in an optical buffer memory for sto...

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Detalles Bibliográficos
Autores principales: Romeira, B., Avó, R., Figueiredo, José M. L., Barland, S., Javaloyes, J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4726037/
https://www.ncbi.nlm.nih.gov/pubmed/26781583
http://dx.doi.org/10.1038/srep19510
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author Romeira, B.
Avó, R.
Figueiredo, José M. L.
Barland, S.
Javaloyes, J.
author_facet Romeira, B.
Avó, R.
Figueiredo, José M. L.
Barland, S.
Javaloyes, J.
author_sort Romeira, B.
collection PubMed
description We investigate a photonic regenerative memory based upon a neuromorphic oscillator with a delayed self-feedback (autaptic) connection. We disclose the existence of a unique temporal response characteristic of localized structures enabling an ideal support for bits in an optical buffer memory for storage and reshaping of data information. We link our experimental implementation, based upon a nanoscale nonlinear resonant tunneling diode driving a laser, to the paradigm of neuronal activity, the FitzHugh-Nagumo model with delayed feedback. This proof-of-concept photonic regenerative memory might constitute a building block for a new class of neuron-inspired photonic memories that can handle high bit-rate optical signals.
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spelling pubmed-47260372016-01-28 Regenerative memory in time-delayed neuromorphic photonic resonators Romeira, B. Avó, R. Figueiredo, José M. L. Barland, S. Javaloyes, J. Sci Rep Article We investigate a photonic regenerative memory based upon a neuromorphic oscillator with a delayed self-feedback (autaptic) connection. We disclose the existence of a unique temporal response characteristic of localized structures enabling an ideal support for bits in an optical buffer memory for storage and reshaping of data information. We link our experimental implementation, based upon a nanoscale nonlinear resonant tunneling diode driving a laser, to the paradigm of neuronal activity, the FitzHugh-Nagumo model with delayed feedback. This proof-of-concept photonic regenerative memory might constitute a building block for a new class of neuron-inspired photonic memories that can handle high bit-rate optical signals. Nature Publishing Group 2016-01-19 /pmc/articles/PMC4726037/ /pubmed/26781583 http://dx.doi.org/10.1038/srep19510 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Romeira, B.
Avó, R.
Figueiredo, José M. L.
Barland, S.
Javaloyes, J.
Regenerative memory in time-delayed neuromorphic photonic resonators
title Regenerative memory in time-delayed neuromorphic photonic resonators
title_full Regenerative memory in time-delayed neuromorphic photonic resonators
title_fullStr Regenerative memory in time-delayed neuromorphic photonic resonators
title_full_unstemmed Regenerative memory in time-delayed neuromorphic photonic resonators
title_short Regenerative memory in time-delayed neuromorphic photonic resonators
title_sort regenerative memory in time-delayed neuromorphic photonic resonators
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4726037/
https://www.ncbi.nlm.nih.gov/pubmed/26781583
http://dx.doi.org/10.1038/srep19510
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