Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, 26506, USA.
Department of Neurosurgery, West Virginia University, Morgantown, WV, 26506, USA.
Commun Biol. 2022 Jun 20;5(1):611. doi: 10.1038/s42003-022-03557-9.
A central challenge in face perception research is to understand how neurons encode face identities. This challenge has not been met largely due to the lack of simultaneous access to the entire face processing neural network and the lack of a comprehensive multifaceted model capable of characterizing a large number of facial features. Here, we addressed this challenge by conducting in silico experiments using a pre-trained face recognition deep neural network (DNN) with a diverse array of stimuli. We identified a subset of DNN units selective to face identities, and these identity-selective units demonstrated generalized discriminability to novel faces. Visualization and manipulation of the network revealed the importance of identity-selective units in face recognition. Importantly, using our monkey and human single-neuron recordings, we directly compared the response of artificial units with real primate neurons to the same stimuli and found that artificial units shared a similar representation of facial features as primate neurons. We also observed a region-based feature coding mechanism in DNN units as in human neurons. Together, by directly linking between artificial and primate neural systems, our results shed light on how the primate brain performs face recognition tasks.
面部识别研究的一个核心挑战是理解神经元如何对人脸身份进行编码。由于缺乏对整个面部处理神经网络的同步访问，以及缺乏能够描述大量面部特征的全面多方面模型，这一挑战尚未得到解决。在这里，我们通过使用具有各种刺激的预先训练的人脸识别深度神经网络（DNN）进行计算机实验来解决这一挑战。我们确定了一组对人脸身份具有选择性的 DNN 单元，这些身份选择性单元对新面孔表现出普遍的可辨别性。对网络的可视化和操作揭示了身份选择性单元在人脸识别中的重要性。重要的是，使用我们的猴子和人类单细胞记录，我们直接比较了人工单元与真实灵长类神经元对相同刺激的反应，发现人工单元与灵长类神经元对面部特征的表示相似。我们还在 DNN 单元中观察到了基于区域的特征编码机制，就像人类神经元一样。总的来说，通过在人工和灵长类神经之间直接建立联系，我们的结果揭示了灵长类大脑如何执行面部识别任务。
