对卷积神经网络的分析揭示了面部表情下皮层处理所必需的计算特性
Chanseok Lim1,2, Mikio Inagaki1,3, Takashi Shinozaki3,4
1Laboratory for Cognitive Neuroscience, Graduate School of Frontier Biosciences, Osaka University, 1-4 Yamadaoka, Suita, Osaka, 565-0871, Japan.
Scientific reports
|July 5, 2023
概括
下皮层的途径.
科学领域:
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
- 计算神经科学是一种神经科学.
背景情况:
- 面部表情感知对于灵长类动物的社会互动至关重要.
- 视觉信息通过腹腔皮层和皮层下路径进行处理.
- 下皮层通路的处理不准确性和架构基础仍然不清楚.
研究的目的:
- 在面部表情识别中研究皮下通路的建筑和生理性质.
- 使用卷积神经网络 (CNN) 建模下皮层处理.
- 阐明特定的皮层下特征在空间频率 (SF) 编码中的作用.
主要方法:
- 构建了模仿皮层下物质的CNN:浅层建筑,同心的受体场和空间聚合.
- 评估了CNN在面部表情分类中的表现.
- 系统地用皮层特征替换下皮层特征,以评估对性能和SF编码的影响.
主要成果:
- 具有皮层下属性的CNN显示了适度的面部表情分类准确度.
- 用皮层特征替换皮层特征逐渐提高了性能.
- 发现皮层下属性限制了精度,并决定了SF表示坐标,影响了基于视网膜和基于对象的SF灵敏度.
结论:
- 三个研究的皮层下属性 (浅层结构,同心接收场,空间聚合) 限制了面部表情识别的准确性.
- 这些属性对于确定空间频率表示坐标至关重要.
- 研究结果描述了皮下回路在面部表情识别中的计算作用.
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