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How can artificial neural networks approximate the brain?

The article reviews the history development of artificial neural networks (ANNs), then compares the differences between ANNs and brain networks in their constituent unit, network architecture, and dynamic principle. The authors offer five points of suggestion for ANNs development and ten questions t...

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Detalles Bibliográficos
Autores principales: Shao, Feng, Shen, Zheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9868316/
https://www.ncbi.nlm.nih.gov/pubmed/36698593
http://dx.doi.org/10.3389/fpsyg.2022.970214
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author Shao, Feng
Shen, Zheng
author_facet Shao, Feng
Shen, Zheng
author_sort Shao, Feng
collection PubMed
description The article reviews the history development of artificial neural networks (ANNs), then compares the differences between ANNs and brain networks in their constituent unit, network architecture, and dynamic principle. The authors offer five points of suggestion for ANNs development and ten questions to be investigated further for the interdisciplinary field of brain simulation. Even though brain is a super-complex system with 10(11) neurons, its intelligence does depend rather on the neuronal type and their energy supply mode than the number of neurons. It might be possible for ANN development to follow a new direction that is a combination of multiple modules with different architecture principle and multiple computation, rather than very large scale of neural networks with much more uniformed units and hidden layers.
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spelling pubmed-98683162023-01-24 How can artificial neural networks approximate the brain? Shao, Feng Shen, Zheng Front Psychol Psychology The article reviews the history development of artificial neural networks (ANNs), then compares the differences between ANNs and brain networks in their constituent unit, network architecture, and dynamic principle. The authors offer five points of suggestion for ANNs development and ten questions to be investigated further for the interdisciplinary field of brain simulation. Even though brain is a super-complex system with 10(11) neurons, its intelligence does depend rather on the neuronal type and their energy supply mode than the number of neurons. It might be possible for ANN development to follow a new direction that is a combination of multiple modules with different architecture principle and multiple computation, rather than very large scale of neural networks with much more uniformed units and hidden layers. Frontiers Media S.A. 2023-01-09 /pmc/articles/PMC9868316/ /pubmed/36698593 http://dx.doi.org/10.3389/fpsyg.2022.970214 Text en Copyright © 2023 Shao and Shen. 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 Psychology
Shao, Feng
Shen, Zheng
How can artificial neural networks approximate the brain?
title How can artificial neural networks approximate the brain?
title_full How can artificial neural networks approximate the brain?
title_fullStr How can artificial neural networks approximate the brain?
title_full_unstemmed How can artificial neural networks approximate the brain?
title_short How can artificial neural networks approximate the brain?
title_sort how can artificial neural networks approximate the brain?
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9868316/
https://www.ncbi.nlm.nih.gov/pubmed/36698593
http://dx.doi.org/10.3389/fpsyg.2022.970214
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