Feed-forward deep neural networks have better performance in object categorization tasks than other models of computer vision. To understand the relationship between feed-forward deep networks and the primate brain, we investigated representations of upright and inverted faces in a convolutional deep neural network model and compared them with representations by neurons in the monkey anterior inferior-temporal cortex, area TE. We applied principal component analysis to feature vectors in each model layer to visualize the relationship between the vectors of the upright and inverted faces. The vectors of the upright and inverted monkey faces were more separated through the convolution layers. In the fully-connected layers, the separation among human individuals for upright faces was larger than for inverted faces. The Spearman correlation between each model layer and TE neurons reached a maximum at the fully-connected layers. These results indicate that the processing of faces in the fully-connected layers might resemble the asymmetric representation of upright and inverted faces by the TE neurons. The separation of upright and inverted faces might take place by feed-forward processing in the visual cortex, and separations among human individuals for upright faces, which were larger than those for inverted faces, might occur in area TE.

前馈深度神经网络在对象分类任务中比其他计算机视觉模型具有更好的性能。为了理解前馈深度网络和灵长类动物大脑之间的关系，我们研究了卷积深度神经网络模型中直立和倒立面部的表示，并将它们与猴子前下颞叶皮层 TE 区域中的神经元表示进行了比较。我们将主成分分析应用于每个模型层中的特征向量，以可视化正立面和倒立面的向量之间的关系。直立和倒立的猴脸的向量通过卷积层分离得更多。在全连接层中，直立人脸的人类个体之间的分离大于倒立人脸。每个模型层与 TE 神经元之间的 Spearman 相关性在全连接层达到最大值。这些结果表明，全连接层中人脸的处理可能类似于 TE 神经元对直立和倒立人脸的不对称表示。直立和倒立人脸的分离可能通过视觉皮层的前馈处理发生，而人类个体之间的直立人脸分离比倒立人脸更大，可能发生在区域TE。