CRCNS: US-Japan Research Proposal: The Computational Principles of a Neural Face Processing System

我们对计算原理和神经机制的理解存在根本性的差距，神经回路是通过这些原理和机制来表示像人脸这样的复杂物体的。这个概念上的差距构成了一个重要的问题，因为，直到它被填补，我们将无法理解人脸识别和脸盲的原因。长期目标是了解人脸识别的计算原理和神经机制，并根据这些原理创建计算机人脸识别系统。这个建议的总体目标是确定在大脑的面部处理网络中的局部和全局面部特征编码的计算原理。中心的假设是，高层次的功能调谐在面对选择性领域可以被理解为刺激空间的统计特性和一般的组织功能的电路，处理这些刺激的结果。这一提议的基本原理是，完成拟议的研究将提供一个理解，神经回路如何产生一个有意义的表示复杂的视觉形状，施加关键约束的视觉理论。该假设将通过追求三个具体目标来测试，这三个目标将确定1）面部特征调谐的神经机制，2）分类面部选择性的神经机制，以及3）特征调谐转换的神经机制。联合计算和实验的方法将整合功能磁共振成像定位面部区域与针对这些区域的电生理记录和模型系统的计算分析。该方法是创新的，通过紧密耦合的理论原理和实验验证，并通过开发新的理论和实验方法。这项研究意义重大，因为它将揭示神经回路功能的原理，这些原理与理解视觉对象识别和多节点网络具有普遍意义。由于研究结果是对社会感知回路机制理解的一个进步，因此它将识别与面孔失明、面孔失认症以及自闭症谱系障碍、脆性X染色体和威廉姆斯综合征等综合征中社会感知改变的理解直接相关的面孔处理系统的脆弱性。

There is a fundamental gap in our understanding of the computational principles and neural mechanisms by which neural circuits represent complex objects like faces. This conceptual gap constitutes an important problem because, until it is filled, we will not be able to understand face recognition and the reasons for face blindness. The long-term goal is to understand the computational principles and neural mechanisms of face recognition and create a computer face-recognition system based on these principles. The overall objective of this proposal is the determination of the computational principles of local and global face feature coding in the brain's face-processing network. The central hypothesis is that high-level feature tuning in face-selective areas can be understood as the result of the statistical properties of stimulus space and general organizational features of the circuits that process these stimuli. The rationale for this proposal is that completion of the proposed research will provide an understanding, of how neural circuits generate a meaningful representation of complex visual shape, imposing critical constraints on theories of vision. The hypothesis will be tested by pursuing three specific aims, which will determine 1) neural mechanism for facial feature tuning, 2) neural mechanism for categorical face selectivity, and 3) neural mechanisms for transformations of feature tuning. The joint computational and experimental approach will integrate functional magnetic resonance imaging to localize face areas with electrophysiological recordings targeted to these regions and computational analyses of model systems. The approach is innovative through the tight coupling of theoretical principles and experimental validation and by developing novel theoretical and experimental methodologies. The proposed research is significant, because it will unravel principles of neural circuit function that are of general relevance for understanding visual object recognition and multi-node networks. Because the outcome is an advance in understanding circuit mechanisms of social perception, it will identify vulnerabilities of the face-processing system directly relevant to the understanding of face blindness, prosopagnosia, and of altered social perception in syndromes spanning autism spectrum disorders, fragile X, and Williams syndrome.