PyLissom : a tool for modeling computational maps of the visual cortex in PyTorch

Autores
Barijhof, Hernán Federico
Año de publicación
2019
Idioma
inglés
Tipo de recurso
tesis de grado
Estado
versión publicada
Colaborador/a o director/a de tesis
Matuk Herrera, Rosana Isabel
Descripción
The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history. Better understanding of biological brains could play a vital role in building intelligent machines. However, communication and collaboration between the two fields has become less commonplace [79]. Computational tools that integrate approaches to neuroscience and machine learning, in accessible and documented form, are very scarce in the literature. The availability of these tools could be fruitful for the interaction between the neuroscience and machine learning communities, and the emergence of new ideas and collaborations. Self-organized neural networks with lateral connections (LISSOM) have been proposed in the literature as a computational model of maps in the visual cortex in primates [84]. These networks were implemented by a group of the University of Edinburgh and the University of Texas in a computational system called Topographica [71]. The use case of the Topographica software has been the neuroscience community. The Topographica software has been used successfully by some researchers to validate computational models in neuroscience. However, due its design, Topographica use has been restricted to neuroscience, and it is very difficult to extend and adapt its code for machine learning uses. In this thesis, LISSOM networks are implemented with a hybrid use case for the machine learning and the neuroscience communities. The software developed in this work, named PyLissom, allows on one hand to build hierarchical models of the visual system, and on the other hand, be used for machine learning applications, since it can combine LISSOM neural networks with other type of artificial neural networks. PyLissom has a modern software design, is implemented in PyTorch and can use GPU optimization.
Fil: Barijhof, Hernán Federico. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Materia
BIO INSPIRED ARTIFICIAL INTELLIGENCE
ARTIFICIAL NEURAL NETWORKS
MACHINE LEARNING
VISUAL CORTEX
COMPUTATIONAL MODELS
PYTHON IN NEUROSCIENCE
PYTORCH
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar
Repositorio
Biblioteca Digital (UBA-FCEN)
Institución
Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
OAI Identificador
seminario:seminario_nCOM000623_Barijhof

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spelling PyLissom : a tool for modeling computational maps of the visual cortex in PyTorchBarijhof, Hernán FedericoBIO INSPIRED ARTIFICIAL INTELLIGENCEARTIFICIAL NEURAL NETWORKSMACHINE LEARNINGVISUAL CORTEXCOMPUTATIONAL MODELSPYTHON IN NEUROSCIENCEPYTORCHThe fields of neuroscience and artificial intelligence (AI) have a long and intertwined history. Better understanding of biological brains could play a vital role in building intelligent machines. However, communication and collaboration between the two fields has become less commonplace [79]. Computational tools that integrate approaches to neuroscience and machine learning, in accessible and documented form, are very scarce in the literature. The availability of these tools could be fruitful for the interaction between the neuroscience and machine learning communities, and the emergence of new ideas and collaborations. Self-organized neural networks with lateral connections (LISSOM) have been proposed in the literature as a computational model of maps in the visual cortex in primates [84]. These networks were implemented by a group of the University of Edinburgh and the University of Texas in a computational system called Topographica [71]. The use case of the Topographica software has been the neuroscience community. The Topographica software has been used successfully by some researchers to validate computational models in neuroscience. However, due its design, Topographica use has been restricted to neuroscience, and it is very difficult to extend and adapt its code for machine learning uses. In this thesis, LISSOM networks are implemented with a hybrid use case for the machine learning and the neuroscience communities. The software developed in this work, named PyLissom, allows on one hand to build hierarchical models of the visual system, and on the other hand, be used for machine learning applications, since it can combine LISSOM neural networks with other type of artificial neural networks. PyLissom has a modern software design, is implemented in PyTorch and can use GPU optimization.Fil: Barijhof, Hernán Federico. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.Universidad de Buenos Aires. Facultad de Ciencias Exactas y NaturalesMatuk Herrera, Rosana Isabel2019info:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_7a1finfo:ar-repo/semantics/tesisDeGradoapplication/pdfhttps://hdl.handle.net/20.500.12110/seminario_nCOM000623_Barijhofenginfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/arreponame:Biblioteca Digital (UBA-FCEN)instname:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturalesinstacron:UBA-FCEN2025-09-29T13:43:40Zseminario:seminario_nCOM000623_BarijhofInstitucionalhttps://digital.bl.fcen.uba.ar/Universidad públicaNo correspondehttps://digital.bl.fcen.uba.ar/cgi-bin/oaiserver.cgiana@bl.fcen.uba.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:18962025-09-29 13:43:41.634Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturalesfalse
dc.title.none.fl_str_mv PyLissom : a tool for modeling computational maps of the visual cortex in PyTorch
title PyLissom : a tool for modeling computational maps of the visual cortex in PyTorch
spellingShingle PyLissom : a tool for modeling computational maps of the visual cortex in PyTorch
Barijhof, Hernán Federico
BIO INSPIRED ARTIFICIAL INTELLIGENCE
ARTIFICIAL NEURAL NETWORKS
MACHINE LEARNING
VISUAL CORTEX
COMPUTATIONAL MODELS
PYTHON IN NEUROSCIENCE
PYTORCH
title_short PyLissom : a tool for modeling computational maps of the visual cortex in PyTorch
title_full PyLissom : a tool for modeling computational maps of the visual cortex in PyTorch
title_fullStr PyLissom : a tool for modeling computational maps of the visual cortex in PyTorch
title_full_unstemmed PyLissom : a tool for modeling computational maps of the visual cortex in PyTorch
title_sort PyLissom : a tool for modeling computational maps of the visual cortex in PyTorch
dc.creator.none.fl_str_mv Barijhof, Hernán Federico
author Barijhof, Hernán Federico
author_facet Barijhof, Hernán Federico
author_role author
dc.contributor.none.fl_str_mv Matuk Herrera, Rosana Isabel
dc.subject.none.fl_str_mv BIO INSPIRED ARTIFICIAL INTELLIGENCE
ARTIFICIAL NEURAL NETWORKS
MACHINE LEARNING
VISUAL CORTEX
COMPUTATIONAL MODELS
PYTHON IN NEUROSCIENCE
PYTORCH
topic BIO INSPIRED ARTIFICIAL INTELLIGENCE
ARTIFICIAL NEURAL NETWORKS
MACHINE LEARNING
VISUAL CORTEX
COMPUTATIONAL MODELS
PYTHON IN NEUROSCIENCE
PYTORCH
dc.description.none.fl_txt_mv The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history. Better understanding of biological brains could play a vital role in building intelligent machines. However, communication and collaboration between the two fields has become less commonplace [79]. Computational tools that integrate approaches to neuroscience and machine learning, in accessible and documented form, are very scarce in the literature. The availability of these tools could be fruitful for the interaction between the neuroscience and machine learning communities, and the emergence of new ideas and collaborations. Self-organized neural networks with lateral connections (LISSOM) have been proposed in the literature as a computational model of maps in the visual cortex in primates [84]. These networks were implemented by a group of the University of Edinburgh and the University of Texas in a computational system called Topographica [71]. The use case of the Topographica software has been the neuroscience community. The Topographica software has been used successfully by some researchers to validate computational models in neuroscience. However, due its design, Topographica use has been restricted to neuroscience, and it is very difficult to extend and adapt its code for machine learning uses. In this thesis, LISSOM networks are implemented with a hybrid use case for the machine learning and the neuroscience communities. The software developed in this work, named PyLissom, allows on one hand to build hierarchical models of the visual system, and on the other hand, be used for machine learning applications, since it can combine LISSOM neural networks with other type of artificial neural networks. PyLissom has a modern software design, is implemented in PyTorch and can use GPU optimization.
Fil: Barijhof, Hernán Federico. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
description The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history. Better understanding of biological brains could play a vital role in building intelligent machines. However, communication and collaboration between the two fields has become less commonplace [79]. Computational tools that integrate approaches to neuroscience and machine learning, in accessible and documented form, are very scarce in the literature. The availability of these tools could be fruitful for the interaction between the neuroscience and machine learning communities, and the emergence of new ideas and collaborations. Self-organized neural networks with lateral connections (LISSOM) have been proposed in the literature as a computational model of maps in the visual cortex in primates [84]. These networks were implemented by a group of the University of Edinburgh and the University of Texas in a computational system called Topographica [71]. The use case of the Topographica software has been the neuroscience community. The Topographica software has been used successfully by some researchers to validate computational models in neuroscience. However, due its design, Topographica use has been restricted to neuroscience, and it is very difficult to extend and adapt its code for machine learning uses. In this thesis, LISSOM networks are implemented with a hybrid use case for the machine learning and the neuroscience communities. The software developed in this work, named PyLissom, allows on one hand to build hierarchical models of the visual system, and on the other hand, be used for machine learning applications, since it can combine LISSOM neural networks with other type of artificial neural networks. PyLissom has a modern software design, is implemented in PyTorch and can use GPU optimization.
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/bachelorThesis
info:eu-repo/semantics/publishedVersion
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info:ar-repo/semantics/tesisDeGrado
format bachelorThesis
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.12110/seminario_nCOM000623_Barijhof
url https://hdl.handle.net/20.500.12110/seminario_nCOM000623_Barijhof
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales
publisher.none.fl_str_mv Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales
dc.source.none.fl_str_mv reponame:Biblioteca Digital (UBA-FCEN)
instname:Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
instacron:UBA-FCEN
reponame_str Biblioteca Digital (UBA-FCEN)
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instname_str Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
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institution UBA-FCEN
repository.name.fl_str_mv Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
repository.mail.fl_str_mv ana@bl.fcen.uba.ar
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