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

CRCNS：美日研究提案：神经人脸处理系统的计算原理

基本信息

批准号：
10016303
负责人：
Winrich Freiwald
金额：
$ 26.6万
依托单位：
ROCKEFELLER UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10016303
关键词：
Address Area Artificial Intelligence Biological Models Brain Brain region Categories Cells Characteristics Classification Code Complex Computer Analysis Computer Models Computers Coupling Data Data Analyses Detection Electrophysiology (science)Face Face Processing Fragile X Syndrome Functional Magnetic Resonance Imaging Generations Goals Head Human Image Impairment Intelligence Japan Joints Learning Life Macaca Masks Methodology Methods Mission Modeling Neurodevelopmental Disorder Neurons Neurosciences Noise Outcome Pattern Primates Principal Component Analysis Process Property Prosopagnosia Public Health Research Research Proposals Shapes Social Perception Stimulus Structure Syndrome System Testing Theoretical Studies United States National Institutes of Health Validation Vision Visual Visual Perception Williams Syndrome Work autism spectrum disorder base cognitive process computational neuroscience computer science deep learning developmental prosopagnosia disability experimental study innovation insight neural circuit neuromechanism neurotechnology novel novel strategies object recognition operation relating to nervous system response sensory system social statistical learning success theories

项目摘要

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.

我们对计算原理和神经机制的理解存在根本的差距，神经回路代表像面部一样复杂的对象。这个概念上的差距构成了一个重要的问题，因为在填补之前，我们将无法理解面部识别和面部失明的原因。长期目标是了解面部识别的计算原理和神经机制，并基于这些原理创建计算机面部识别系统。该提案的总体目的是确定大脑面部处理网络中本地和全球面部特征编码的计算原理。中心假设是，可以将面部选择区域中的高级特征调整为刺激空间的统计特性以及处理这些刺激的电路的一般组织特征的结果。该提案的理由是，拟议研究的完成将提供理解，即神经回路如何产生对复杂视觉形状的有意义的表示，从而对视觉理论施加关键的限制。该假设将通过追求三个特定目标来检验，这将确定1）面部特征调整的神经机制，2）用于分类面部选择性的神经机制，以及3）特征调整转换的神经机制。联合计算和实验方法将将功能磁共振成像与针对这些区域的电生理记录以及模型系统的计算分析进行了将面部区域的位置。通过紧密的理论原理和实验验证以及开发新颖的理论和实验方法，这种方法是创新的。拟议的研究很重要，因为它将阐明与理解视觉对象识别和多节点网络有关的神经回路功能的原理。由于结果是理解社会感知的电路机制的进步，因此它将确定面部处理系统的脆弱性与理解面部失明，prosopagnosia以及跨越自闭症谱系疾病，脆弱X和Williams Cyndrome的综合症的社会感知改变有关。