{"id":38958,"date":"2018-03-09T09:38:48","date_gmt":"2018-03-09T09:38:48","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/modelo-neuronal-artificial-multi-dendritico-de-unidades-con-campo-receptivo\/"},"modified":"2018-03-09T09:38:48","modified_gmt":"2018-03-09T09:38:48","slug":"modelo-neuronal-artificial-multi-dendritico-de-unidades-con-campo-receptivo","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/fisica\/modelo-neuronal-artificial-multi-dendritico-de-unidades-con-campo-receptivo\/","title":{"rendered":"Modelo neuronal artificial multi-dendritico de unidades con campo receptivo."},"content":{"rendered":"<h2>Tesis doctoral de <strong> Julio David Buldain P\u00e9rez <\/strong><\/h2>\n<p>La tesis se centra en la descripci\u00f3n y simulaci\u00f3n de un paradigma neuronal artificial. Las redes neuronales de este modelo adoptan arquitecturas en base a una descripci\u00f3n de la conectividad entre capas de tipo multi-dendr\u00edtica, donde cada capa de la red conecta con sus entradas desde otras capas estableciendo haces dendr\u00edticos de conexiones. Ese tipo de descripci\u00f3n arquitectural permite la selecci\u00f3n de vias procesadoras en la red mediante habilitaci\u00f3n-inhabilitaci\u00f3n de haces dendr\u00edticos.  la funcionalidad de las unidades o neuronas artificiales implementadas en las simulaciones est\u00e1 basada en la definici\u00f3n de campos receptivos dendr\u00edticos. La fase de recuerdo del sistema neuronal sigue una din\u00e1mica ascendente en las capas de la red, cuyo proceso en las neuronas consiste en: estimulaci\u00f3n, evaluaci\u00f3n de excitaciones dendr\u00edticas y c\u00e1lculo de la respuesta de salida (sigmoide multidendr\u00edtica normalizada).  el proceso de aprendizaje puede ser de tipo auto-organizado o supervisado. La auto- organizaci\u00f3n se determina por una din\u00e1mica competitiva (tipo k-wta) cuyo mecanismo de consciencia es una variante del algoritmo fscl. La supervisi\u00f3n puede implementarse de dos formas: directamente, por asignaci\u00f3n de etiquetas de supervisi\u00f3n a las activaciones de competici\u00f3n de las unidades, o asociativamente, mediante v\u00edas dentr\u00edticas auto-organizadas estimuladas con las etiquetas de supervisi\u00f3n.  las simulaciones muestran las capacidades computacionales de los diversos m\u00f3dulos de arquitectura, principalmente en problemas de clasificaci\u00f3n de patrones. Se pueden aplicar a la ortogonalizaci\u00f3n de los est\u00edmulos (m\u00f3dulo seguidor), asociaci\u00f3n de est\u00edmulos (m\u00f3dulo seguidor-asociativo), diversificaci\u00f3n de las particiones del espacio de entrada (plano diferenciador) y su recombinaci\u00f3n en nuevas particiones (m\u00f3dulo diferenciador-integrador).  las diversas capacidades computacionales de estos m\u00f3dulos permiten ori<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Modelo neuronal artificial multi-dendritico de unidades con campo receptivo.<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Modelo neuronal artificial multi-dendritico de unidades con campo receptivo. <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Julio David Buldain P\u00e9rez <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Zaragoza<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 14\/12\/1998<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Direcci\u00f3n y tribunal<\/h3>\n<ul>\n<li><strong>Director de la tesis<\/strong>\n<ul>\n<li>Armando Roy Yarza<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: roberto Moreno diaz <\/li>\n<li>joan Cabestany moncasi (vocal)<\/li>\n<li>Antonio Nu\u00f1ez ordo\u00f1ez (vocal)<\/li>\n<li>Antonio  Jes\u00fas Torralba silgado (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Julio David Buldain P\u00e9rez La tesis se centra en la descripci\u00f3n y simulaci\u00f3n de un paradigma neuronal [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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