Una propuesta de neurona artificial: la Unidad Neuro-Vascular Artificial (UNVA)

Las 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...

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Fecha de publicación:
2022
Institución:
Universidad del Rosario
Repositorio:
Repositorio EdocUR - U. Rosario
Idioma:
spa
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oai:repository.urosario.edu.co:10336/33792
Acceso en línea:
https://doi.org/10.48713/10336_33792
https://repository.urosario.edu.co/handle/10336/33792
Palabra clave:
Neurona
Acoplamiento neurovascular
Neurona Artificial
Perceptrón
Descenso del gradiente
Tasa de aprendizaje
Estabilidad
Sistema dinámico
Unidad Neuro-Vascular Artificial
Medicina experimental
Neuron
Neurovascular coupling
Artificial neuron
Perceptron
Gradient descent
Learning rate
Stability
Dynamic system
Artificial Neuro-Vascular Unit
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License
Atribución-NoComercial-CompartirIgual 2.5 Colombia
id EDOCUR2_28235283ae6e12ee20d9aa0aff4a52c4
oai_identifier_str oai:repository.urosario.edu.co:10336/33792
network_acronym_str EDOCUR2
network_name_str Repositorio EdocUR - U. Rosario
repository_id_str
dc.title.es.fl_str_mv Una propuesta de neurona artificial: la Unidad Neuro-Vascular Artificial (UNVA)
dc.title.TranslatedTitle.es.fl_str_mv A proposal of artificial neuron: the Artificial Neuro-Vascular Unit (ANVU)
title Una propuesta de neurona artificial: la Unidad Neuro-Vascular Artificial (UNVA)
spellingShingle Una propuesta de neurona artificial: la Unidad Neuro-Vascular Artificial (UNVA)
Neurona
Acoplamiento neurovascular
Neurona Artificial
Perceptrón
Descenso del gradiente
Tasa de aprendizaje
Estabilidad
Sistema dinámico
Unidad Neuro-Vascular Artificial
Medicina experimental
Neuron
Neurovascular coupling
Artificial neuron
Perceptron
Gradient descent
Learning rate
Stability
Dynamic system
Artificial Neuro-Vascular Unit
title_short Una propuesta de neurona artificial: la Unidad Neuro-Vascular Artificial (UNVA)
title_full Una propuesta de neurona artificial: la Unidad Neuro-Vascular Artificial (UNVA)
title_fullStr Una propuesta de neurona artificial: la Unidad Neuro-Vascular Artificial (UNVA)
title_full_unstemmed Una propuesta de neurona artificial: la Unidad Neuro-Vascular Artificial (UNVA)
title_sort Una propuesta de neurona artificial: la Unidad Neuro-Vascular Artificial (UNVA)
dc.contributor.advisor.none.fl_str_mv Caicedo Dorado, Alexander
dc.subject.es.fl_str_mv Neurona
Acoplamiento neurovascular
Neurona Artificial
Perceptrón
Descenso del gradiente
Tasa de aprendizaje
Estabilidad
Sistema dinámico
Unidad Neuro-Vascular Artificial
topic Neurona
Acoplamiento neurovascular
Neurona Artificial
Perceptrón
Descenso del gradiente
Tasa de aprendizaje
Estabilidad
Sistema dinámico
Unidad Neuro-Vascular Artificial
Medicina experimental
Neuron
Neurovascular coupling
Artificial neuron
Perceptron
Gradient descent
Learning rate
Stability
Dynamic system
Artificial Neuro-Vascular Unit
dc.subject.ddc.es.fl_str_mv Medicina experimental
dc.subject.keyword.es.fl_str_mv Neuron
Neurovascular coupling
Artificial neuron
Perceptron
Gradient descent
Learning rate
Stability
Dynamic system
Artificial Neuro-Vascular Unit
description Las 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.
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-03-01T15:46:54Z
dc.date.available.none.fl_str_mv 2022-03-01T15:46:54Z
dc.date.created.none.fl_str_mv 2022-02-23
dc.type.eng.fl_str_mv bachelorThesis
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
dc.type.document.es.fl_str_mv Trabajo de grado
dc.type.spa.spa.fl_str_mv Trabajo de grado
dc.identifier.doi.none.fl_str_mv https://doi.org/10.48713/10336_33792
dc.identifier.uri.none.fl_str_mv https://repository.urosario.edu.co/handle/10336/33792
url https://doi.org/10.48713/10336_33792
https://repository.urosario.edu.co/handle/10336/33792
dc.language.iso.es.fl_str_mv spa
language spa
dc.rights.*.fl_str_mv Atribución-NoComercial-CompartirIgual 2.5 Colombia
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.acceso.es.fl_str_mv Abierto (Texto Completo)
dc.rights.uri.*.fl_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/co/
rights_invalid_str_mv Atribución-NoComercial-CompartirIgual 2.5 Colombia
Abierto (Texto Completo)
http://creativecommons.org/licenses/by-nc-sa/2.5/co/
http://purl.org/coar/access_right/c_abf2
dc.format.extent.es.fl_str_mv 80 pp
dc.format.mimetype.es.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidad del Rosario
dc.publisher.department.none.fl_str_mv Escuela de Ingeniería, Ciencia y Tecnología
dc.publisher.program.none.fl_str_mv Programa de Matemáticas Aplicadas y Ciencias de la Computación - MACC
publisher.none.fl_str_mv Universidad del Rosario
institution Universidad del Rosario
dc.source.bibliographicCitation.none.fl_str_mv Shaurya, Manu (2019) McCulloch-Pitts Neuron vs Perceptron model.
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spelling Caicedo Dorado, Alexander14139512600Ruiz Ortiz, Juan CamiloProfesional en Matemáticas Aplicadas y Ciencias de la ComputaciónPregradoFull timeba69b607-6ab0-4849-9226-fe75975c9e0b6002022-03-01T15:46:54Z2022-03-01T15:46:54Z2022-02-23Las 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.Artificial 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.80 ppapplication/pdfhttps://doi.org/10.48713/10336_33792 https://repository.urosario.edu.co/handle/10336/33792spaUniversidad del RosarioEscuela de Ingeniería, Ciencia y TecnologíaPrograma de Matemáticas Aplicadas y Ciencias de la Computación - MACCAtribución-NoComercial-CompartirIgual 2.5 ColombiaAbierto (Texto Completo)http://creativecommons.org/licenses/by-nc-sa/2.5/co/http://purl.org/coar/access_right/c_abf2Shaurya, Manu (2019) McCulloch-Pitts Neuron vs Perceptron model. McCulloch, W; Pitts, W (1943) A Logical Calculus of the Ideas Immanent in Nervous Activity. En: Bulletin of Mathematical Biology. Vol. 52; No. 1, 2; pp. 99-115 Disponible en: http://dx.doi.org/10.1007/BF02478259. Disponible en: 10.1007/BF02478259.Wikipedia,; Red neuronal artificial. 2020/11/20; Consultado en: 2020/11/20.Wikipedia,; Hopfield (RNA). 2020/5/13; Consultado en: 2020/5/13.Pelphrey, Kevin A; Volkmar, Fred R (2013) Blood-Oxygen-Level-Dependent (BOLD) Signal. En: Encyclopedia of Autism Spectrum Disorders. pp. 465-466 New York, NY: Springer New York; 9781441916983; Disponible en: https://doi.org/10.1007/978-1-4419-1698-3_550; http://dx.doi.org/10.1007/978-1-4419-1698-3_550. Disponible en: 10.1007/978-1-4419-1698-3_550.Dunn, J-Oc; Mythen, M G; Grocott, M P (2016) Physiology of oxygen transport. En: BJA Education. Vol. 16; No. 10; pp. 341-348 2058-5349; Disponible en: https://doi.org/10.1093/bjaed/mkw012; http://dx.doi.org/10.1093/bjaed/mkw012. Disponible en: 10.1093/bjaed/mkw012.Manning, John R (1961) Diffusion in a Chemical Concentration Gradient. En: Phys. Rev. Vol. 124; pp. 470-482 : American Physical Society; Disponible en: https://link.aps.org/doi/10.1103/PhysRev.124.470; http://dx.doi.org/10.1103/PhysRev.124.470. Disponible en: 10.1103/PhysRev.124.470.Unit, Mrc Mitochondrial Biology; What are mitochondria. Huneau, Clément; Benali, Habib; Chabriat, Hugues (2015) Investigating Human Neurovascular Coupling Using Functional Neuroimaging:. Vol. 9; pp. 467 1662-453X; Disponible en: https://www.frontiersin.org/article/10.3389/fnins.2015.00467; http://dx.doi.org/10.3389/fnins.2015.00467. Disponible en: 10.3389/fnins.2015.00467.Riera, Jorge J; Wan, Xiaohong; Jimenez, Juan Carlos; Kawashima, Ryuta (2006) Nonlinear local electrovascular coupling. I: A theoretical model. En: Human brain mapping. Vol. 27; No. 11; pp. 896-914 : Wiley Online Library;Zayane, Chadia; Laleg-Kirati, Taous Meriem (2015) A sensitivity analysis of fMRI balloon model. En: Computational and mathematical methods in medicine. Vol. 2015; Hindawi;Schaper, Charles D (2019) Analytic Model of fMRI BOLD Signals for Separable Metrics of Neural and. En: bioRxiv.: Cold Spring Harbor Laboratory; Disponible en: https://www.biorxiv.org/content/early/2019/03/09/573006; http://dx.doi.org/10.1101/573006. Disponible en: 10.1101/573006. Sistema nervioso central. Disponible en: https://www.significados.com/sistema-nervioso-central/.Jasvinder Chawla, Thomas R; Gest, Ashmeet Singh Sahni; Peripheral Nervous System Anatomy. Disponible en: https://emedicine.medscape.com/article/1948687-overview.Omar Islam, Mahan Mathur; Brain Magnetic Resonance Imaging. Disponible en: https://emedicine.medscape.com/article/2105033-overview.Wikipedia contributors (2021) Functional magnetic resonance imaging --- Wikipedia, The Free Encyclopedia. Disponible en: https://en.wikipedia.org/w/index.php?title=Functional_magnetic_resonance_imaging&oldid=1016553270.Wikipedia contributors (2021) List of systems of the human body --- Wikipedia, The Free Encyclopedia. Disponible en: https://en.wikipedia.org/w/index.php?title=List_of_systems_of_the_human_body&oldid=1019327234.Lodish, H; Berk, A; Zipursky, S L; al., Et (2000) Overview of Neuron Structure and Function. Disponible en: https://www.ncbi.nlm.nih.gov/books/NBK21535/.Chambers, David; Huang, Christopher; Matthews, Gareth (2015) Nerve action potential and propagation. En: Basic Physiology for Anaesthetists. pp. 221–227 : Cambridge University Press; Disponible en: http://dx.doi.org/10.1017/CBO9781139226394.051. Disponible en: 10.1017/CBO9781139226394.051.Khanna, Aarushi (2021) Action Potential. 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