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dc.contributor.advisorCaicedo Dorado, Alexander 
dc.creatorRuiz Ortiz, Juan Camilo 
dc.date.accessioned2022-03-01T15:46:54Z
dc.date.available2022-03-01T15:46:54Z
dc.date.created2022-02-23
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/33792
dc.descriptionLas neuronas artificiales son un modelo computacional simplificado de cómo funcionan las neuronas biológicas presentes en el cerebro. Sin embargo, los modelos de las primeras neuronas artificiales se fundamentaron únicamente en el procesamiento de información proveniente de señales eléctricas, y no tuvieron en cuenta los cambios vasculares necesarios que permiten entregar nutrientes a las neuronas para que funcionen correctamente, en particular durante su activación eléctrica. Por lo tanto, en esta tesis se propone un nuevo modelo computacional que considera tanto el comportamiento eléctrico como el vascular. Para diseñar la nueva arquitectura, se revisaron las condiciones de estabilidad del descenso del gradiente. Este análisis nos permite definir cotas superiores para la tasa de aprendizaje. Una vez propuesta la arquitectura se evaluó su comportamiento comparado con algoritmos más tradicionales como la regresión lineal.
dc.description.abstractArtificial neurons are a simplified computational model of biological neurons which are present in the brain. However, the first artificial neuron models were based only on the information processing that comes from electric signals and did not include vascular changes that allow the supply of nutrients to the neurons for their correct functioning, particularly during their electric activation. Therefore, in this thesis, a new computational model is proposed that considers the electric and vascular behavior. To design this new architecture, the stability conditions of gradient descent were reviewed. This analysis allowed us to declare upper bounds for the learning rate. Once the architecture was proposed, its behavior was compared with more traditional algorithms like linear regression.
dc.format.extent80 pp
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.rightsAtribución-NoComercial-CompartirIgual 2.5 Colombia
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/2.5/co/
dc.subjectNeurona
dc.subjectAcoplamiento neurovascular
dc.subjectNeurona Artificial
dc.subjectPerceptrón
dc.subjectDescenso del gradiente
dc.subjectTasa de aprendizaje
dc.subjectEstabilidad
dc.subjectSistema dinámico
dc.subjectUnidad Neuro-Vascular Artificial
dc.subject.ddcMedicina experimental 
dc.titleUna propuesta de neurona artificial: la Unidad Neuro-Vascular Artificial (UNVA)
dc.typebachelorThesis
dc.publisherUniversidad del Rosario
dc.creator.degreeProfesional en Matemáticas Aplicadas y Ciencias de la Computación
dc.publisher.programPrograma de Matemáticas Aplicadas y Ciencias de la Computación - MACC
dc.publisher.departmentEscuela de Ingeniería, Ciencia y Tecnología
dc.subject.keywordNeuron
dc.subject.keywordNeurovascular coupling
dc.subject.keywordArtificial neuron
dc.subject.keywordPerceptron
dc.subject.keywordGradient descent
dc.subject.keywordLearning rate
dc.subject.keywordStability
dc.subject.keywordDynamic system
dc.subject.keywordArtificial Neuro-Vascular Unit
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.type.spaTrabajo de grado
dc.rights.accesoAbierto (Texto Completo)
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersion
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dc.type.documentTrabajo de grado
dc.identifier.doihttps://doi.org/10.48713/10336_33792
dc.creator.degreetypeFull time
dc.title.TranslatedTitleA proposal of artificial neuron: the Artificial Neuro-Vascular Unit (ANVU)
dc.source.instnameinstname:Universidad del Rosario
dc.source.reponamereponame:Repositorio Institucional EdocUR
dc.creator.degreeLevelPregrado
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