NADOL: Neuromorphic Architecture for Spike-Driven Online Learning by Dendrites

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Authors: Yang, Shuangming, Wang, Haowen, Pang, Yanwei, Azghadi, Mostafa, R., Linares-Barranco, Bernabé
Format: article
Status:Versión aceptada para publicación
Publication Date:2024
Country:España
Institution:Consejo Superior de Investigaciones Científicas (CSIC)
Repository:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/390745
Online Access:http://hdl.handle.net/10261/390745
https://api.elsevier.com/content/abstract/scopus_id/85183574595
Access Level:Open access
Keyword:Dendritic learning
Neuromorphic
Online learning
Spike-driven learning
Spiking neural network (SNN)
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spelling NADOL: Neuromorphic Architecture for Spike-Driven Online Learning by DendritesYang, ShuangmingWang, HaowenPang, YanweiAzghadi, Mostafa, R.Linares-Barranco, BernabéDendritic learningNeuromorphicOnline learningSpike-driven learningSpiking neural network (SNN)© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Biologically plausible learning with neuronal dendrites is a promising perspective to improve the spike-driven learning capability by introducing dendritic processing as an additional hyperparameter. Neuromorphic computing is an effective and essential solution towards spike-based machine intelligence and neural learning systems. However, on-line learning capability for neuromorphic models is still an open challenge. In this study a novel neuromorphic architecture with dendritic on-line learning (NADOL) is presented, which is a novel efficient methodology for brain-inspired intelligence on embedded hardware. With the feature of distributed processing using spiking neural network, NADOL can cut down the power consumption and enhance the learning efficiency and convergence speed. A detailed analysis for NADOL is presented, which demonstrates the effects of different conditions on learning capabilities, including neuron number in hidden layer, dendritic segregation parameters, feedback connection, and connection sparseness with various levels of amplification. Piecewise linear approximation approach is used to cut down the computational resource cost. The experimental results demonstrate a remarkable learning capability that surpasses other solutions, with NADOL exhibiting superior performance over the GPU platform in dendritic learning. This study's applicability extends across diverse domains, including the Internet of Things, robotic control, and brain-machine interfaces. Moreover, it signifies a pivotal step in bridging the gap between artificial intelligence and neuroscience through the introduction of an innovative neuromorphic paradigm.This work was supported partly by the National Key Research and Development Program of China (Grant No. 2022ZD0160405), and supported in part by the National Natural Science Foundation of China (Grant No. 62006170, 62376185, and 62176179), and partly by Young Elite Scientists Sponsorship Program by CAST (2022QNRC001)Peer reviewedInstitute of Electrical and Electronics EngineersNational Key Research and Development Program (China)National Natural Science Foundation of ChinaChina Association for Science and TechnologyYang, Shuangming [0000-0002-8044-0860]Wang, Haowen [0009-0000-9073-5331]Pang, Yanwei [0000-0001-6670-3727]Azghadi, Mostafa, R. [0000-0001-7975-3985]Linares-Barranco, Bernabé [0000-0002-1813-4889]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttp://hdl.handle.net/10261/390745https://api.elsevier.com/content/abstract/scopus_id/85183574595reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttps://doi.org/10.1109/TBCAS.2023.3316968Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3907452026-05-22T06:33:51Z
dc.title.none.fl_str_mv NADOL: Neuromorphic Architecture for Spike-Driven Online Learning by Dendrites
title NADOL: Neuromorphic Architecture for Spike-Driven Online Learning by Dendrites
spellingShingle NADOL: Neuromorphic Architecture for Spike-Driven Online Learning by Dendrites
Yang, Shuangming
Dendritic learning
Neuromorphic
Online learning
Spike-driven learning
Spiking neural network (SNN)
title_short NADOL: Neuromorphic Architecture for Spike-Driven Online Learning by Dendrites
title_full NADOL: Neuromorphic Architecture for Spike-Driven Online Learning by Dendrites
title_fullStr NADOL: Neuromorphic Architecture for Spike-Driven Online Learning by Dendrites
title_full_unstemmed NADOL: Neuromorphic Architecture for Spike-Driven Online Learning by Dendrites
title_sort NADOL: Neuromorphic Architecture for Spike-Driven Online Learning by Dendrites
dc.creator.none.fl_str_mv Yang, Shuangming
Wang, Haowen
Pang, Yanwei
Azghadi, Mostafa, R.
Linares-Barranco, Bernabé
author Yang, Shuangming
author_facet Yang, Shuangming
Wang, Haowen
Pang, Yanwei
Azghadi, Mostafa, R.
Linares-Barranco, Bernabé
author_role author
author2 Wang, Haowen
Pang, Yanwei
Azghadi, Mostafa, R.
Linares-Barranco, Bernabé
author2_role author
author
author
author
dc.contributor.none.fl_str_mv National Key Research and Development Program (China)
National Natural Science Foundation of China
China Association for Science and Technology
Yang, Shuangming [0000-0002-8044-0860]
Wang, Haowen [0009-0000-9073-5331]
Pang, Yanwei [0000-0001-6670-3727]
Azghadi, Mostafa, R. [0000-0001-7975-3985]
Linares-Barranco, Bernabé [0000-0002-1813-4889]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Dendritic learning
Neuromorphic
Online learning
Spike-driven learning
Spiking neural network (SNN)
topic Dendritic learning
Neuromorphic
Online learning
Spike-driven learning
Spiking neural network (SNN)
description © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/390745
https://api.elsevier.com/content/abstract/scopus_id/85183574595
url http://hdl.handle.net/10261/390745
https://api.elsevier.com/content/abstract/scopus_id/85183574595
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://doi.org/10.1109/TBCAS.2023.3316968
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,198674

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