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

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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]. Compu...

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Autor principal: Barijhof, Hernán Federico
Otros Autores: Matuk Herrera, Rosana Isabel
Formato: Tesis de grado publishedVersion
Lenguaje:Inglés
Publicado: Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales 2019
Materias:
BIO INSPIRED ARTIFICIAL INTELLIGENCE
ARTIFICIAL NEURAL NETWORKS
MACHINE LEARNING
VISUAL CORTEX
COMPUTATIONAL MODELS
PYTHON IN NEUROSCIENCE
PYTORCH
Acceso en línea:https://hdl.handle.net/20.500.12110/seminario_nCOM000623_Barijhof
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Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id seminario:seminario_nCOM000623_Barijhof
record_format dspace
spelling seminario:seminario_nCOM000623_Barijhof2025-08-08T16:50:26Z PyLissom : a tool for modeling computational maps of the visual cortex in PyTorch Barijhof, Hernán Federico Matuk Herrera, Rosana Isabel BIO INSPIRED ARTIFICIAL INTELLIGENCE ARTIFICIAL NEURAL NETWORKS MACHINE LEARNING VISUAL CORTEX COMPUTATIONAL MODELS PYTHON IN NEUROSCIENCE PYTORCH 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. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales 2019 info:eu-repo/semantics/bachelorThesis info:ar-repo/semantics/tesis de grado info:eu-repo/semantics/publishedVersion application/pdf eng info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar https://hdl.handle.net/20.500.12110/seminario_nCOM000623_Barijhof
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
language Inglés
orig_language_str_mv eng
topic BIO INSPIRED ARTIFICIAL INTELLIGENCE
ARTIFICIAL NEURAL NETWORKS
MACHINE LEARNING
VISUAL CORTEX
COMPUTATIONAL MODELS
PYTHON IN NEUROSCIENCE
PYTORCH
spellingShingle BIO INSPIRED ARTIFICIAL INTELLIGENCE
ARTIFICIAL NEURAL NETWORKS
MACHINE LEARNING
VISUAL CORTEX
COMPUTATIONAL MODELS
PYTHON IN NEUROSCIENCE
PYTORCH
Barijhof, Hernán Federico
PyLissom : a tool for modeling computational maps of the visual cortex in PyTorch
topic_facet BIO INSPIRED ARTIFICIAL INTELLIGENCE
ARTIFICIAL NEURAL NETWORKS
MACHINE LEARNING
VISUAL CORTEX
COMPUTATIONAL MODELS
PYTHON IN NEUROSCIENCE
PYTORCH
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.
author2 Matuk Herrera, Rosana Isabel
author_facet Matuk Herrera, Rosana Isabel
Barijhof, Hernán Federico
format Tesis de grado
Tesis de grado
publishedVersion
author Barijhof, Hernán Federico
author_sort Barijhof, Hernán Federico
title PyLissom : a tool for modeling computational maps of the visual cortex in 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
publisher Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales
publishDate 2019
url https://hdl.handle.net/20.500.12110/seminario_nCOM000623_Barijhof
work_keys_str_mv AT barijhofhernanfederico pylissomatoolformodelingcomputationalmapsofthevisualcortexinpytorch
_version_ 1843125818900873216

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