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:
- 金额:$ 26.6万
- 依托单位:
- 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:
- 关键词:AddressAreaArtificial IntelligenceBiological ModelsBrainBrain regionCategoriesCellsCharacteristicsClassificationCodeComplexComputer AnalysisComputer ModelsComputersCouplingDataData AnalysesDetectionElectrophysiology (science)FaceFace ProcessingFragile X SyndromeFunctional Magnetic Resonance ImagingGenerationsGoalsHeadHumanImageImpairmentIntelligenceJapanJointsLearningLifeMacacaMasksMethodologyMethodsMissionModelingNeurodevelopmental DisorderNeuronsNeurosciencesNoiseOutcomePatternPrimatesPrincipal Component AnalysisProcessPropertyProsopagnosiaPublic HealthResearchResearch ProposalsShapesSocial PerceptionStimulusStructureSyndromeSystemTestingTheoretical StudiesUnited States National Institutes of HealthValidationVisionVisualVisual PerceptionWilliams SyndromeWorkautism spectrum disorderbasecognitive processcomputational neurosciencecomputer sciencedeep learningdevelopmental prosopagnosiadisabilityexperimental studyinnovationinsightneural circuitneuromechanismneurotechnologynovelnovel strategiesobject recognitionoperationrelating to nervous systemresponsesensory systemsocialstatistical learningsuccesstheories
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.
我们对神经回路表示复杂物体(如人脸)的计算原理和神经机制的理解存在根本性的差距。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症的原因。长期目标是了解人脸识别的计算原理和神经机制,并基于这些原理创建计算机人脸识别系统。本研究的总体目标是确定大脑人脸处理网络中局部和全局人脸特征编码的计算原理。核心假设是,在面部选择区域的高级特征调谐可以被理解为刺激空间的统计特性和处理这些刺激的电路的一般组织特征的结果。这项提议的基本原理是,完成这项提议的研究将提供一种理解,神经回路如何产生复杂视觉形状的有意义的表示,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验证,这将决定1)面部特征调整的神经机制,2)分类面部选择的神经机制,以及3)特征调整转换的神经机制。联合计算和实验方法将结合功能磁共振成像来定位面部区域,针对这些区域的电生理记录和模型系统的计算分析。通过理论原理和实验验证的紧密耦合以及开发新的理论和实验方法,该方法具有创新性。这项研究具有重要意义,因为它将揭示神经回路功能的原理,这些原理对于理解视觉对象识别和多节点网络具有普遍意义。由于这一结果是对社会知觉回路机制理解的一个进步,它将确定面部处理系统的脆弱性,这些脆弱性与理解面部盲症、面孔失认症以及跨越自闭症谱系障碍、脆性X和威廉姆斯综合征的综合征的社会知觉改变直接相关。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Winrich Freiwald其他文献
Winrich Freiwald的其他文献
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{{ truncateString('Winrich Freiwald', 18)}}的其他基金
Revealing the mechanisms of primate face recognition with synthetic stimulus sets optimized to compare computational models
通过优化比较计算模型的合成刺激集揭示灵长类动物面部识别的机制
- 批准号:
10524626 - 财政年份:2022
- 资助金额:
$ 26.6万 - 项目类别:
Genetic dissection of cortical projection neurons in social brain circuits
社会脑回路中皮质投射神经元的基因解剖
- 批准号:
10452678 - 财政年份:2021
- 资助金额:
$ 26.6万 - 项目类别:
Genetic dissection of cortical projection neurons in social brain circuits
社会脑回路中皮质投射神经元的基因解剖
- 批准号:
10303553 - 财政年份:2021
- 资助金额:
$ 26.6万 - 项目类别:
Uncovering the Functional Organization and Cell Type Composition of Cortical Face Areas
揭示面部皮质区域的功能组织和细胞类型组成
- 批准号:
10227904 - 财政年份:2020
- 资助金额:
$ 26.6万 - 项目类别:
Defining the Neural Circuits of Attention Control: A New Hypothesis
定义注意力控制的神经回路:一个新假设
- 批准号:
10356859 - 财政年份:2020
- 资助金额:
$ 26.6万 - 项目类别:
Defining the Neural Circuits of Attention Control: A New Hypothesis
定义注意力控制的神经回路:一个新假设
- 批准号:
10576288 - 财政年份:2020
- 资助金额:
$ 26.6万 - 项目类别:
Motor Compositionality in the Control of Facial Movements
控制面部运动的运动组合性
- 批准号:
10599085 - 财政年份:2019
- 资助金额:
$ 26.6万 - 项目类别:
Motor Compositionality in the Control of Facial Movements
控制面部运动的运动组合性
- 批准号:
10374011 - 财政年份:2019
- 资助金额:
$ 26.6万 - 项目类别:
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
- 批准号:
9765324 - 财政年份:2018
- 资助金额:
$ 26.6万 - 项目类别:
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