Cargando…
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...
Autores principales: | , |
---|---|
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 |
_version_ | 1784876507553857536 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9868316 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT shaofeng howcanartificialneuralnetworksapproximatethebrain AT shenzheng howcanartificialneuralnetworksapproximatethebrain |