人类视觉区域的脑优化深度神经网络模型学习非层次的表示方式
Ghislain St-Yves1,2, Emily J Allen3, Yihan Wu4
1Department of Neuroscience, University of Minnesota, Minneapolis, MN, 55455, USA.
Nature communications
|June 7, 2023
概括
深度神经网络 (DNN) 不需要等级表示来预测人脑在视觉区域的活动. DNN可以使用各种架构准确预测大脑活动,而不仅仅是严格的层次结构.
科学领域:
- 计算神经科学是一种计算神经科学.
- 认知神经科学是一种认知神经科学.
- 人工智能的人工智能是人工智能.
背景情况:
- 经过对视觉任务的训练,深度神经网络 (DNN) 会发展出与灵长类视觉皮层组织相仿的层次表征.
- 一种普遍的解释表明,这些等级表示对于准确建模灵长类动物视觉系统活动至关重要.
研究的目的:
- 调查分层表示是否是准确预测早期视觉区域 (V1-V4) 人类大脑活动的必要组成部分.
- 将单分支与多分支DNN架构在人类视觉皮层的fMRI数据上的预测能力进行比较.
主要方法:
- 训练有素的深度神经网络 (DNN) 直接预测通过功能磁共振成像 (fMRI) 在人类视觉区域V1-V4.4测量的大脑活动.
- 采用了两个不同的DNN架构:一个单分支网络共同预测所有区域,一个多分支网络独立预测每个区域.
主要成果:
- 多分支DNN,独立预测视觉区域,证明了学习等级表示的能力.
- 然而,只有单个分支的DNN,共同预测所有视觉区域,内在开发了层次的表示.
- 两种架构都在V1-V4中实现了对人类大脑活动的准确预测,而不考虑明确的层次结构.
结论:
- 深度神经网络 (DNN) 对于准确预测人类大脑在视觉区域V1-V4.4的活动,等级表示并不严格必要.
- 编码类似大脑的视觉表示的DNN可以展示各种架构,包括串行层次结构和独立处理分支.
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