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dc.creatorPeng, Honges
dc.creatorBao, Tingtinges
dc.creatorLuo, Xiaohuies
dc.creatorWang, Junes
dc.creatorSong, Xiaoxiaoes
dc.creatorRiscos Núñez, Agustínes
dc.creatorPérez Jiménez, Mario de Jesúses
dc.date.accessioned2021-04-26T11:33:02Z
dc.date.available2021-04-26T11:33:02Z
dc.date.issued2020
dc.identifier.citationPeng, H., Bao, T., Luo, X., Wang, J., Song, X., Riscos Núñez, A. y Pérez Jiménez, M.d.J. (2020). Dendrite P systems. Neural Networks, 127 (July 2020), 110-120.
dc.identifier.issn0893-6080es
dc.identifier.urihttps://hdl.handle.net/11441/107808
dc.description.abstractIt was recently found that dendrites are not just a passive channel. They can perform mixed computation of analog and digital signals, and therefore can be abstracted as information processors. Moreover, dendrites possess a feedback mechanism. Motivated by these computational and feedback characteristics, this article proposes a new variant of neural-like P systems, dendrite P (DeP) systems, where neurons simulate the computational function of dendrites and perform a firing–storing process instead of the storing–firing process in spiking neural P (SNP) systems. Moreover, the behavior of the neurons is characterized by dendrite rules that are abstracted by two characteristics of dendrites. Different from the usual firing rules in SNP systems, the firing of a dendrite rule is controlled by the states of the corresponding source neurons. Therefore, DeP systems can provide a collaborative control capability for neurons. We discuss the computational power of DeP systems. In particular, it is proven that DeP systems are Turing-universal number generating/accepting devices. Moreover, we construct a small universal DeP system consisting of 115 neurons for computing functions.es
dc.description.sponsorshipResearch Fund of Sichuan Science and Technology No. 2018JY0083es
dc.description.sponsorshipResearch Foundation of the Education Department of Sichuan No. 17TD0034es
dc.formatapplication/pdfes
dc.format.extent11es
dc.language.isoenges
dc.publisherElsevieres
dc.relation.ispartofNeural Networks, 127 (July 2020), 110-120.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectP systemses
dc.subjectNeural-like P systemses
dc.subjectDendrite P systemses
dc.subjectComputational poweres
dc.titleDendrite P systemses
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/submittedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificiales
dc.relation.projectIDNo. 2018JY0083es
dc.relation.projectIDNo. 17TD0034es
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0893608020301349es
dc.identifier.doi10.1016/j.neunet.2020.04.014es
dc.contributor.groupUniversidad de Sevilla. TIC193: Computación Naturales
dc.journaltitleNeural Networkses
dc.publication.volumen127es
dc.publication.issueJuly 2020es
dc.publication.initialPage110es
dc.publication.endPage120es
dc.contributor.funderResearch Fund of Sichuan Science and Technologyes
dc.contributor.funderResearch Foundation of the Education Department of Sichuanes

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