dc.creator | Peng, Hong | es |
dc.creator | Bao, Tingting | es |
dc.creator | Luo, Xiaohui | es |
dc.creator | Wang, Jun | es |
dc.creator | Song, Xiaoxiao | es |
dc.creator | Riscos Núñez, Agustín | es |
dc.creator | Pérez Jiménez, Mario de Jesús | es |
dc.date.accessioned | 2021-04-26T11:33:02Z | |
dc.date.available | 2021-04-26T11:33:02Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Peng, 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.issn | 0893-6080 | es |
dc.identifier.uri | https://hdl.handle.net/11441/107808 | |
dc.description.abstract | It 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.sponsorship | Research Fund of Sichuan Science and Technology No. 2018JY0083 | es |
dc.description.sponsorship | Research Foundation of the Education Department of Sichuan No. 17TD0034 | es |
dc.format | application/pdf | es |
dc.format.extent | 11 | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Neural Networks, 127 (July 2020), 110-120. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | P systems | es |
dc.subject | Neural-like P systems | es |
dc.subject | Dendrite P systems | es |
dc.subject | Computational power | es |
dc.title | Dendrite P systems | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial | es |
dc.relation.projectID | No. 2018JY0083 | es |
dc.relation.projectID | No. 17TD0034 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0893608020301349 | es |
dc.identifier.doi | 10.1016/j.neunet.2020.04.014 | es |
dc.contributor.group | Universidad de Sevilla. TIC193: Computación Natural | es |
dc.journaltitle | Neural Networks | es |
dc.publication.volumen | 127 | es |
dc.publication.issue | July 2020 | es |
dc.publication.initialPage | 110 | es |
dc.publication.endPage | 120 | es |
dc.contributor.funder | Research Fund of Sichuan Science and Technology | es |
dc.contributor.funder | Research Foundation of the Education Department of Sichuan | es |