Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/53219
Campo DC Valoridioma
dc.contributor.authorMoreno-Díaz, R.en_US
dc.contributor.authorQuesada-Arencibia, Aen_US
dc.contributor.otherQuesada-Arencibia, Alexis-
dc.date.accessioned2019-02-04T15:40:40Z-
dc.date.available2019-02-04T15:40:40Z-
dc.date.issued1999en_US
dc.identifier.isbn978-3-211-83364-3en_US
dc.identifier.urihttp://hdl.handle.net/10553/53219-
dc.description.abstractPresynaptic Inhibition (PI) basically consists of the strong suppression of a neuron’s response before the stimulus reaches the synaptic terminals mediated by a second, inhibitory, neuron. It has a long lasting effect, greatly potentiated by the action of anaesthetics, that has been observed in motorneurons and in several other places of nervous systems, mainly in sensory processing. In this paper we will focus on several different ways of modelling the effect of PI in the visual pathway as well as the different artificial counterparts derived from such modelling, mainly in two directions: the possibility of computing invariant representations against general changes in illumination of the input image impinging the retina (which is equivalent to a low-level non linear information processing filter) and the role of PI as selector of sets of stimulae that have to be derived to higher brain areas, which, in turn, is equivalent to a “higher-level filter” of information, in the sense of “filtering” the possible semantic content of the information that is allowed to reach later stages of processing.en_US
dc.languageengen_US
dc.sourceArtificial Neural Nets And Genetic Algorithms, p. 40-45en_US
dc.subject120304 Inteligencia artificialen_US
dc.subject220990 Tratamiento digital. Imágenesen_US
dc.titleThe role and modeling of presynaptic inhibition in the visual pathway: applications in image processingen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.typeConferenceObject
dc.relation.conferenceInternational Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA 99)
dc.identifier.doi10.1007/978-3-7091-6384-9_8en_US
dc.identifier.isi000084220700008-
dcterms.isPartOfArtificial Neural Nets And Genetic Algorithms-
dcterms.sourceArtificial Neural Nets And Genetic Algorithms, p. 40-45-
dc.description.lastpage45en_US
dc.description.firstpage40en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Actas de congresosen_US
dc.identifier.wosWOS:000084220700008-
dc.contributor.daisngid1339968-
dc.contributor.daisngid1554939
dc.contributor.daisngid1279635-
dc.identifier.investigatorRIDG-2656-2016-
dc.identifier.externalWOS:000084220700008-
dc.identifier.eisbn978-3-7091-6384-9-
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:Moreno-Diaz, R
dc.contributor.wosstandardWOS:Quesada-Arencibia, A
dc.date.coverdate1999
dc.identifier.conferenceidevents120268
dc.identifier.ulpgces
item.grantfulltextnone-
item.fulltextSin texto completo-
crisitem.author.deptGIR IUCES: Computación inteligente, percepción y big data-
crisitem.author.deptIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.deptGIR IUCES: Computación inteligente, percepción y big data-
crisitem.author.deptIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.orcid0000-0002-5314-6033-
crisitem.author.orcid0000-0002-8313-5124-
crisitem.author.parentorgIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.parentorgIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.fullNameMoreno Díaz, Roberto-
crisitem.author.fullNameQuesada Arencibia, Francisco Alexis-
crisitem.event.eventsstartdate06-04-1999-
crisitem.event.eventsenddate09-04-1999-
Colección:Actas de congresos
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