Explaining face representation in the primate brain using different computational models.
Explaining face representation in the primate brain using different computational models.
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DOI：
10.1016/j.cub.2021.04.014
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发表时间：
2021-07-12
期刊：
影响因子：
--
通讯作者：
Tsao DY
中科院分区：
文献类型：
--
作者：
Chang L;Egger B;Vetter T;Tsao DY
理解大脑如何表征复杂物体的身份是视觉神经科学的核心挑战。在猕猴面部补丁系统（一个专门用于面部处理的下颞叶（IT）皮层的子网络）中，已经广泛研究了对象处理的原理。先前的一项研究报告说，单个面部斑块神经元编码一个生成模型的轴，称为“主动外观”模型，该模型将分别代表面部形状和面部纹理的50维特征向量转换为面部图像。然而，系统的研究比较这个模型与其他计算模型，特别是卷积神经网络模型，已成功地解释在腹侧视觉流的神经反应，一直缺乏。在这里，我们记录了细胞的反应，在最前面的脸补丁AM一个大的真实的脸的图像集，并比较了大量的模型来解释神经反应。我们发现，除了CORnet-Z之外，主动外观模型比任何其他模型都更好地解释了反应，CORnet-Z是一种在一般对象分类上训练的前馈深度神经网络，用于对非面部图像进行分类，其性能在某些面部图像集上表现出色，并超过了其他模型。令人惊讶的是，专门针对面部识别训练的深度神经网络并不能很好地解释神经反应。一个主要原因是，与神经元不同，网络中的单元较少受到与面部识别无关的面部相关因素（如照明）的调制。
Understanding how the brain represents the identity of complex objects is a central challenge of visual neuroscience. The principles governing object processing have been extensively studied in the macaque face patch system, a sub-network of inferotemporal (IT) cortex specialized for face processing. A previous study reported that single face patch neurons encode axes of a generative model called the “active appearance” model, which transforms 50-d feature vectors separately representing facial shape and facial texture into facial images. However, a systematic investigation comparing this model to other computational models, especially convolutional neural network models that have shown success in explaining neural responses in the ventral visual stream, has been lacking. Here, we recorded responses of cells in the most anterior face patch AM to a large set of real face images and compared a large number of models for explaining neural responses. We found that the active appearance model better explained responses than any other model except CORnet-Z, a feedforward deep neural network trained on general object classification to classify non-face images, whose performance it tied on some face image sets and exceeded on others. Surprisingly, deep neural networks trained specifically on facial identification did not explain neural responses well. A major reason is that units in the network, unlike neurons, are less modulated by face-related factors unrelated to facial identification such as illumination.
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DOI：
10.1126/science.1194908
发表时间：
2010-11-05
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
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通讯作者：
Tsao DY
影响因子：
5.4
作者：
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作者：
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通讯作者：
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DOI：
10.1109/tsmca.2007.909557
发表时间：
2008-01-01
影响因子：
--
作者：
Gao, Wen;Cao, Bo;Zhao, Debin
通讯作者：
Zhao, Debin
影响因子：
3.4
作者：
Kalfas I;Kumar S;Vogels R
通讯作者：
Vogels R