Convergent evolution of face spaces across human face-selective neuronal groups and deep convolutional networks
Convergent evolution of face spaces across human face-selective neuronal groups and deep convolutional networks
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DOI：
10.1038/s41467-019-12623-6
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发表时间：
2019-10-30
影响因子：
16.6
通讯作者：
Malach, Rafael
中科院分区：
文献类型：
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作者：
Grossman, Shany;Gaziv, Guy;Malach, Rafael
深度卷积神经网络（DCNN）在现实任务中实现人类表现的发现为将神经元调谐特性与此类任务联系起来提供了新的机会。在这里，我们表明，面部空间的几何形状，揭示了通过成对的激活相似性的面部选择性神经元组颅内记录在33例患者，显着匹配的DCNN具有人类水平的人脸识别能力。这种跨越生物和人工网络的模式相似性的趋同进化突出了面部空间几何在面部感知中的重要性。此外，神经元与DCNN匹配的性质表明人脸区域在人脸感知的图像方面的作用。首先，匹配仅限于中间DCNN层。其次，向DCNN呈现身份保留图像操作消除了其与神经元反应的相关性。最后，与人类神经元组调谐匹配的DCNN单元显示视点选择性感受野。我们的研究结果表明，人脸空间的几何图形方面的重要性，人类的面孔感知。
The discovery that deep convolutional neural networks (DCNNs) achieve human performance in realistic tasks offers fresh opportunities for linking neuronal tuning properties to such tasks. Here we show that the face-space geometry, revealed through pair-wise activation similarities of face-selective neuronal groups recorded intracranially in 33 patients, significantly matches that of a DCNN having human-level face recognition capabilities. This convergent evolution of pattern similarities across biological and artificial networks highlights the significance of face-space geometry in face perception. Furthermore, the nature of the neuronal to DCNN match suggests a role of human face areas in pictorial aspects of face perception. First, the match was confined to intermediate DCNN layers. Second, presenting identity-preserving image manipulations to the DCNN abolished its correlation to neuronal responses. Finally, DCNN units matching human neuronal group tuning displayed view-point selective receptive fields. Our results demonstrate the importance of face-space geometry in the pictorial aspects of human face perception.