Neural Mechanisms of Face Recognition

描述（由申请人提供）：对人脸识别的理解存在根本性的差距：为什么大脑中有多个人脸神经元代码，以及它们如何通过相互连接的人脸处理区域网络进行转换以支持人脸识别？由于面孔在社会上的重要性，这种差距的持续存在构成了一个重要的问题，因为在填补这一差距之前，物体识别的神经机制在很大程度上仍然是不可理解的。这项研究的长期目标是在一个与人类相似的高度进化的人脸处理系统中获得对人脸识别的机械理解。这个特殊的应用程序的总体目标是identifiy的规则和机制的信息神经元之间的皮质节点的面部处理网络的转换。拟议的工作将测试的中心假设，该网络被组织为一个信息处理层次服务强大的人脸识别，其中每个处理级别执行一个特定的脸的转换。它利用了模型系统的功能组织，模型系统由空间上不同但相互关联的 具有独特功能专门化的节点-以及这些节点由于其对已知视觉对象类别（面部）的选择性而易于用脑成像识别的事实。 这个建议的基本原理是，在完成这项研究后，我们将在计算，表征和机械水平上理解高级对象识别的核心操作。在强有力的初步数据的指导下，中心假设将通过追求三个具体目标进行测试：1）面部细胞使用什么视觉特征来表示复杂的面部信息？2)面部区域如何相互作用以生成高维面部代码？3)人脸区域在人脸编码和人脸检测中的因果作用是什么？在第一个目标下，我们将把三个脑成像识别的面部区域的联合收割机单单位电生理记录与参数视觉刺激结合起来，以揭示单细胞用于编码面部信息的计算机制。在第二个目标下，将分析来自多个区域的联合电生理记录，以揭示区域间的相互作用如何产生不同区域并随时间变化的面部表征。在目标3下，靶向失活将被用来揭示不同的面部加工区域在信息转换和面部检测中所起的因果作用。所提出的研究是有意义的，因为它有望直接显示信息处理网络是如何组织的，以转换高级对象类别的视觉表示，并利用它们的视觉行为。通过这样做，它将把我们对视觉物体识别的理解提升到一个新的水平。提出的研究是概念和方法创新，因为它以系统的角度来看待问题的对象识别，通过多节点网络跟踪信息的转换和集成，通过一种新的组合方法，分析单细胞机制和人口代码与因果关系的网络功能的询问。

DESCRIPTION (provided by applicant): There is a fundamental gap in understanding of face recognition: why are there multiple neuronal codes for faces in the brain, and how they are transformed through a network of interconnected face-processing areas to support face recognition? Continued existence of this gap constitutes an important problem because of the social importance of faces and because, until it is filled, the neural mechanisms for object recognition remain largely incomprehensible. The long-term goal of the proposed research is to gain a mechanistic understanding of face-recognition in a highly evolved face-processing system similar to that of humans. The overall objective of this particular application is to identiy the rules and mechanisms of informational neuronal transformations between the cortical nodes of the face-processing network. The proposed work will test the central hypothesis that this network is organized as an information-processing hierarchy serving robust face-recognition, in which each processing level performs a face-specific transformation. It takes advantage of the functional organization of the model system that consists of spatially distinct, but interconnected nodes with unique functional specializations - and the fact that these nodes are readily identifiable with brain imaging due to their selectivity for a known visual object category, faces. The rationale of this proposal is that, after completion of this research, we will understand core operations of high-level object recognition at a computational, representational, and mechanistic level. Guided by strong preliminary data, the central hypothesis will be tested by pursuing three specific aims: 1) What visual features do face cells use to represent complex facial information? 2) How do face areas interact to generate high-dimensional facial codes? 3) What is the causal role of face areas for facial coding and face detection? Under the first aim, we will combine single unit electrophysiological recordings in three brain-imaging identified face areas with parametric visual stimulation to reveal the computational mechanisms single cells use to code facial information. Under the second aim, joint electrophysiological recordings from multiple areas will be analyzed to reveal how inter-areal interactions generate face-representations that differ across areas and change over time. Under aim 3, targeted inactivation will be used to reveal the causal role different face processing areas play for informational transformations and for face detection. The proposed research is significant, because it is expected to directly show how an information processing network is organized to transform visual representations of a high-level object category and utilizes them for visual behavior. In doing so it will lift our understanding of visual object recognition to a new level. The research proposed is conceptually and methodologically innovative because it in order to take a systems perspective to the problem of object recognition, tracing transformations of information through a multi-node network and integrating, through a novel combination of methodologies, analyses of single cell mechanisms and population codes with causal interrogation of network function.