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Constructing optimized binary masks for reservoir computing with delay systems
Reservoir computing is a novel bio-inspired computing method, capable of solving complex tasks in a computationally efficient way. It has recently been successfully implemented using delayed feedback systems, allowing to reduce the hardware complexity of brain-inspired computers drastically. In this...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3887384/ https://www.ncbi.nlm.nih.gov/pubmed/24406849 http://dx.doi.org/10.1038/srep03629 |
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author | Appeltant, Lennert Van der Sande, Guy Danckaert, Jan Fischer, Ingo |
author_facet | Appeltant, Lennert Van der Sande, Guy Danckaert, Jan Fischer, Ingo |
author_sort | Appeltant, Lennert |
collection | PubMed |
description | Reservoir computing is a novel bio-inspired computing method, capable of solving complex tasks in a computationally efficient way. It has recently been successfully implemented using delayed feedback systems, allowing to reduce the hardware complexity of brain-inspired computers drastically. In this approach, the pre-processing procedure relies on the definition of a temporal mask which serves as a scaled time-mutiplexing of the input. Originally, random masks had been chosen, motivated by the random connectivity in reservoirs. This random generation can sometimes fail. Moreover, for hardware implementations random generation is not ideal due to its complexity and the requirement for trial and error. We outline a procedure to reliably construct an optimal mask pattern in terms of multipurpose performance, derived from the concept of maximum length sequences. Not only does this ensure the creation of the shortest possible mask that leads to maximum variability in the reservoir states for the given reservoir, it also allows for an interpretation of the statistical significance of the provided training samples for the task at hand. |
format | Online Article Text |
id | pubmed-3887384 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-38873842014-01-10 Constructing optimized binary masks for reservoir computing with delay systems Appeltant, Lennert Van der Sande, Guy Danckaert, Jan Fischer, Ingo Sci Rep Article Reservoir computing is a novel bio-inspired computing method, capable of solving complex tasks in a computationally efficient way. It has recently been successfully implemented using delayed feedback systems, allowing to reduce the hardware complexity of brain-inspired computers drastically. In this approach, the pre-processing procedure relies on the definition of a temporal mask which serves as a scaled time-mutiplexing of the input. Originally, random masks had been chosen, motivated by the random connectivity in reservoirs. This random generation can sometimes fail. Moreover, for hardware implementations random generation is not ideal due to its complexity and the requirement for trial and error. We outline a procedure to reliably construct an optimal mask pattern in terms of multipurpose performance, derived from the concept of maximum length sequences. Not only does this ensure the creation of the shortest possible mask that leads to maximum variability in the reservoir states for the given reservoir, it also allows for an interpretation of the statistical significance of the provided training samples for the task at hand. Nature Publishing Group 2014-01-10 /pmc/articles/PMC3887384/ /pubmed/24406849 http://dx.doi.org/10.1038/srep03629 Text en Copyright © 2014, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by-nc-sa/3.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-ShareALike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/ |
spellingShingle | Article Appeltant, Lennert Van der Sande, Guy Danckaert, Jan Fischer, Ingo Constructing optimized binary masks for reservoir computing with delay systems |
title | Constructing optimized binary masks for reservoir computing with delay systems |
title_full | Constructing optimized binary masks for reservoir computing with delay systems |
title_fullStr | Constructing optimized binary masks for reservoir computing with delay systems |
title_full_unstemmed | Constructing optimized binary masks for reservoir computing with delay systems |
title_short | Constructing optimized binary masks for reservoir computing with delay systems |
title_sort | constructing optimized binary masks for reservoir computing with delay systems |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3887384/ https://www.ncbi.nlm.nih.gov/pubmed/24406849 http://dx.doi.org/10.1038/srep03629 |
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