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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Bibliographic Details
Published inFrontiers in psychology Vol. 13; p. 970214
Main Authors Shao, Feng, Shen, Zheng
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Media S.A 09.01.2023
Subjects
dual neural node
emergent computation
energy source mode
hierarchical architecture
neuron type
Psychology
spike-time encoding
emergent computation
energy source mode
executive control to socially cognitive behavior
neuron type
hierarchical architecture
dual neural node
spike-time encoding
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Summary: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 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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Reviewed by: Quan Gu, Zhejiang University, China; Haojiang Ying, Soochow University, China
This article was submitted to Cognitive Science, a section of the journal Frontiers in Psychology
Edited by: Yiannis Laouris, Future Worlds Center, Cyprus
ISSN:1664-1078
1664-1078
DOI:10.3389/fpsyg.2022.970214