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Modeling Neurodegeneration in silico With Deep Learning

Deep neural networks, inspired by information processing in the brain, can achieve human-like performance for various tasks. However, research efforts to use these networks as models of the brain have primarily focused on modeling healthy brain function so far. In this work, we propose a paradigm fo...

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Autores principales: Tuladhar, Anup, Moore, Jasmine A., Ismail, Zahinoor, Forkert, Nils D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8640525/
https://www.ncbi.nlm.nih.gov/pubmed/34867256
http://dx.doi.org/10.3389/fninf.2021.748370
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author Tuladhar, Anup
Moore, Jasmine A.
Ismail, Zahinoor
Forkert, Nils D.
author_facet Tuladhar, Anup
Moore, Jasmine A.
Ismail, Zahinoor
Forkert, Nils D.
author_sort Tuladhar, Anup
collection PubMed
description Deep neural networks, inspired by information processing in the brain, can achieve human-like performance for various tasks. However, research efforts to use these networks as models of the brain have primarily focused on modeling healthy brain function so far. In this work, we propose a paradigm for modeling neural diseases in silico with deep learning and demonstrate its use in modeling posterior cortical atrophy (PCA), an atypical form of Alzheimer’s disease affecting the visual cortex. We simulated PCA in deep convolutional neural networks (DCNNs) trained for visual object recognition by randomly injuring connections between artificial neurons. Results showed that injured networks progressively lost their object recognition capability. Simulated PCA impacted learned representations hierarchically, as networks lost object-level representations before category-level representations. Incorporating this paradigm in computational neuroscience will be essential for developing in silico models of the brain and neurological diseases. The paradigm can be expanded to incorporate elements of neural plasticity and to other cognitive domains such as motor control, auditory cognition, language processing, and decision making.
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spelling pubmed-86405252021-12-04 Modeling Neurodegeneration in silico With Deep Learning Tuladhar, Anup Moore, Jasmine A. Ismail, Zahinoor Forkert, Nils D. Front Neuroinform Neuroscience Deep neural networks, inspired by information processing in the brain, can achieve human-like performance for various tasks. However, research efforts to use these networks as models of the brain have primarily focused on modeling healthy brain function so far. In this work, we propose a paradigm for modeling neural diseases in silico with deep learning and demonstrate its use in modeling posterior cortical atrophy (PCA), an atypical form of Alzheimer’s disease affecting the visual cortex. We simulated PCA in deep convolutional neural networks (DCNNs) trained for visual object recognition by randomly injuring connections between artificial neurons. Results showed that injured networks progressively lost their object recognition capability. Simulated PCA impacted learned representations hierarchically, as networks lost object-level representations before category-level representations. Incorporating this paradigm in computational neuroscience will be essential for developing in silico models of the brain and neurological diseases. The paradigm can be expanded to incorporate elements of neural plasticity and to other cognitive domains such as motor control, auditory cognition, language processing, and decision making. Frontiers Media S.A. 2021-11-19 /pmc/articles/PMC8640525/ /pubmed/34867256 http://dx.doi.org/10.3389/fninf.2021.748370 Text en Copyright © 2021 Tuladhar, Moore, Ismail and Forkert. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Tuladhar, Anup
Moore, Jasmine A.
Ismail, Zahinoor
Forkert, Nils D.
Modeling Neurodegeneration in silico With Deep Learning
title Modeling Neurodegeneration in silico With Deep Learning
title_full Modeling Neurodegeneration in silico With Deep Learning
title_fullStr Modeling Neurodegeneration in silico With Deep Learning
title_full_unstemmed Modeling Neurodegeneration in silico With Deep Learning
title_short Modeling Neurodegeneration in silico With Deep Learning
title_sort modeling neurodegeneration in silico with deep learning
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8640525/
https://www.ncbi.nlm.nih.gov/pubmed/34867256
http://dx.doi.org/10.3389/fninf.2021.748370
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