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:	Anup Tuladhar, Jasmine A. Moore, Zahinoor Ismail, Nils D. Forkert
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	deep neural networks (DNN) posterior cortical atrophy (PCA) neurodegenerative diseases Alzheimer’s disease in silico simulation visual object recognition Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/dc3f4ca0168a451daf3727e49504414c
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spelling	oai:doaj.org-article:dc3f4ca0168a451daf3727e49504414c2021-11-19T07:54:39ZModeling Neurodegeneration in silico With Deep Learning1662-519610.3389/fninf.2021.748370https://doaj.org/article/dc3f4ca0168a451daf3727e49504414c2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fninf.2021.748370/fullhttps://doaj.org/toc/1662-5196Deep 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.Anup TuladharAnup TuladharJasmine A. MooreJasmine A. MooreJasmine A. MooreZahinoor IsmailZahinoor IsmailZahinoor IsmailZahinoor IsmailZahinoor IsmailNils D. ForkertNils D. ForkertNils D. ForkertNils D. ForkertFrontiers Media S.A.articledeep neural networks (DNN)posterior cortical atrophy (PCA)neurodegenerative diseasesAlzheimer’s diseasein silico simulationvisual object recognitionNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neuroinformatics, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep neural networks (DNN) posterior cortical atrophy (PCA) neurodegenerative diseases Alzheimer’s disease in silico simulation visual object recognition Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	deep neural networks (DNN) posterior cortical atrophy (PCA) neurodegenerative diseases Alzheimer’s disease in silico simulation visual object recognition Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Anup Tuladhar Anup Tuladhar Jasmine A. Moore Jasmine A. Moore Jasmine A. Moore Zahinoor Ismail Zahinoor Ismail Zahinoor Ismail Zahinoor Ismail Zahinoor Ismail Nils D. Forkert Nils D. Forkert Nils D. Forkert Nils D. Forkert Modeling Neurodegeneration in silico With Deep Learning
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.
format	article
author	Anup Tuladhar Anup Tuladhar Jasmine A. Moore Jasmine A. Moore Jasmine A. Moore Zahinoor Ismail Zahinoor Ismail Zahinoor Ismail Zahinoor Ismail Zahinoor Ismail Nils D. Forkert Nils D. Forkert Nils D. Forkert Nils D. Forkert
author_facet	Anup Tuladhar Anup Tuladhar Jasmine A. Moore Jasmine A. Moore Jasmine A. Moore Zahinoor Ismail Zahinoor Ismail Zahinoor Ismail Zahinoor Ismail Zahinoor Ismail Nils D. Forkert Nils D. Forkert Nils D. Forkert Nils D. Forkert
author_sort	Anup Tuladhar
title	Modeling Neurodegeneration in silico With Deep Learning
title_short	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_sort	modeling neurodegeneration in silico with deep learning
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/dc3f4ca0168a451daf3727e49504414c
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Modeling Neurodegeneration in silico With Deep Learning

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