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
- Institución
- Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales
- OAI Identificador
- seminario:seminario_nCOM000623_Barijhof
Ver los metadatos del registro completo
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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 http://purl.org/coar/resource_type/c_7a1f 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 |
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Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales |
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Biblioteca Digital (UBA-FCEN) - Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales |
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