人类大脑与人工神经网络之间的等级对应的评估:一篇评论
Trung Quang Pham1, Teppei Matsui2, Junichi Chikazoe1
1Araya Inc., Tokyo 101-0025, Japan.
Biology
|October 27, 2023
概括
人工神经网络 (ANN) 显示出对理解大脑的希望,但由于结构和训练算法的差异,直接比较具有挑战性. 本综述探讨了评估大脑-ANN对应的方法.
科学领域:
- 计算神经科学是一种计算神经科学.
- 人工智能的人工智能是人工智能.
背景情况:
- 人工神经网络 (ANN) 模仿人类大脑功能,并达到人类水平的性能.
- 网络网络为理解大脑信息处理提供了一个潜在的框架.
研究的目的:
- 审查目前评估人工神经网络与人类大脑之间的对应关系的方法.
- 要突出评估大脑-ANN相似性的现有方法的局限性.
主要方法:
- 关于大脑-ANN比较的现有文献的审查.
- 分析不同级别的信息层次,以评估通信.
- 讨论方法上的挑战和局限性.
主要成果:
- 对大脑-ANN对应的直接测量是不可能的.
- 在规模 (神经元与节点) 和训练机制 (例如,反向传播) 中存在显著差异.
- 用多种间接方法来评估不同层次的相似性.
结论:
- 评估大脑-ANN对应需要不同的方法,因为固有的差异.
- 需要进一步开发方法来弥合ANN和神经科学之间的差距.
- 了解这些差异对于在神经科学研究中利用ANN至关重要.
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