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Artificial Neuron Based on Integrated Semiconductor Quantum Dot Mode-Locked Lasers

Neuro-inspired implementations have attracted strong interest as a power efficient and robust alternative to the digital model of computation with a broad range of applications. Especially, neuro-mimetic systems able to produce and process spike-encoding schemes can offer merits like high noise-resi...

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Autores principales: Mesaritakis, Charis, Kapsalis, Alexandros, Bogris, Adonis, Syvridis, Dimitris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5171909/
https://www.ncbi.nlm.nih.gov/pubmed/27991574
http://dx.doi.org/10.1038/srep39317
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author Mesaritakis, Charis
Kapsalis, Alexandros
Bogris, Adonis
Syvridis, Dimitris
author_facet Mesaritakis, Charis
Kapsalis, Alexandros
Bogris, Adonis
Syvridis, Dimitris
author_sort Mesaritakis, Charis
collection PubMed
description Neuro-inspired implementations have attracted strong interest as a power efficient and robust alternative to the digital model of computation with a broad range of applications. Especially, neuro-mimetic systems able to produce and process spike-encoding schemes can offer merits like high noise-resiliency and increased computational efficiency. Towards this direction, integrated photonics can be an auspicious platform due to its multi-GHz bandwidth, its high wall-plug efficiency and the strong similarity of its dynamics under excitation with biological spiking neurons. Here, we propose an integrated all-optical neuron based on an InAs/InGaAs semiconductor quantum-dot passively mode-locked laser. The multi-band emission capabilities of these lasers allows, through waveband switching, the emulation of the excitation and inhibition modes of operation. Frequency-response effects, similar to biological neural circuits, are observed just as in a typical two-section excitable laser. The demonstrated optical building block can pave the way for high-speed photonic integrated systems able to address tasks ranging from pattern recognition to cognitive spectrum management and multi-sensory data processing.
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spelling pubmed-51719092016-12-28 Artificial Neuron Based on Integrated Semiconductor Quantum Dot Mode-Locked Lasers Mesaritakis, Charis Kapsalis, Alexandros Bogris, Adonis Syvridis, Dimitris Sci Rep Article Neuro-inspired implementations have attracted strong interest as a power efficient and robust alternative to the digital model of computation with a broad range of applications. Especially, neuro-mimetic systems able to produce and process spike-encoding schemes can offer merits like high noise-resiliency and increased computational efficiency. Towards this direction, integrated photonics can be an auspicious platform due to its multi-GHz bandwidth, its high wall-plug efficiency and the strong similarity of its dynamics under excitation with biological spiking neurons. Here, we propose an integrated all-optical neuron based on an InAs/InGaAs semiconductor quantum-dot passively mode-locked laser. The multi-band emission capabilities of these lasers allows, through waveband switching, the emulation of the excitation and inhibition modes of operation. Frequency-response effects, similar to biological neural circuits, are observed just as in a typical two-section excitable laser. The demonstrated optical building block can pave the way for high-speed photonic integrated systems able to address tasks ranging from pattern recognition to cognitive spectrum management and multi-sensory data processing. Nature Publishing Group 2016-12-19 /pmc/articles/PMC5171909/ /pubmed/27991574 http://dx.doi.org/10.1038/srep39317 Text en Copyright © 2016, The Author(s) 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
Mesaritakis, Charis
Kapsalis, Alexandros
Bogris, Adonis
Syvridis, Dimitris
Artificial Neuron Based on Integrated Semiconductor Quantum Dot Mode-Locked Lasers
title Artificial Neuron Based on Integrated Semiconductor Quantum Dot Mode-Locked Lasers
title_full Artificial Neuron Based on Integrated Semiconductor Quantum Dot Mode-Locked Lasers
title_fullStr Artificial Neuron Based on Integrated Semiconductor Quantum Dot Mode-Locked Lasers
title_full_unstemmed Artificial Neuron Based on Integrated Semiconductor Quantum Dot Mode-Locked Lasers
title_short Artificial Neuron Based on Integrated Semiconductor Quantum Dot Mode-Locked Lasers
title_sort artificial neuron based on integrated semiconductor quantum dot mode-locked lasers
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5171909/
https://www.ncbi.nlm.nih.gov/pubmed/27991574
http://dx.doi.org/10.1038/srep39317
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