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人工神经网络如何模拟大脑?

How can artificial neural networks approximate the brain?

作者信息

Shao Feng, Shen Zheng

机构信息

Beijing Key Laboratory of Behavior and Mental Health, School of Psychological and Cognitive Sciences, Peking University, Beijing, China.

出版信息

Front Psychol. 2023 Jan 9;13:970214. doi: 10.3389/fpsyg.2022.970214. eCollection 2022.

Abstract

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.

摘要

本文回顾了人工神经网络(ANNs)的历史发展,然后比较了ANNs与脑网络在组成单元、网络架构和动态原理方面的差异。作者针对ANNs的发展提出了五点建议,并针对脑模拟跨学科领域提出了十个有待进一步研究的问题。尽管大脑是一个拥有10个神经元的超级复杂系统,但其智能并非取决于神经元的数量,而是更多地取决于神经元类型及其能量供应模式。ANNs的发展有可能遵循一个新的方向,即结合具有不同架构原理的多个模块和多种计算方式,而不是构建具有更多统一单元和隐藏层的超大规模神经网络。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3391/9868316/8c657c6f2d42/fpsyg-13-970214-g001.jpg

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