Neural coding of complex 3D shape

复杂 3D 形状的神经编码

• 批准号: 6957043
• 负责人: CHARLES E CONNOR
• 金额: $ 32.62万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-15 至 2009-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6957043
• 关键词: Macaca mulatta; behavioral /social science research tag; neural information processing; neuropsychology; single cell analysis; temporal lobe /cortex; visual cortex; visual perception

DESCRIPTION (provided by applicant): Our ability to live within and interact with a world composed of 3D objects depends largely on our spectacular capacity for visual shape perception. This is what makes vision so critical to our health, happiness, and survival. The long-term goal of this project is to understand 3D object perception by discovering the neural code for complex 3D shape in the primate ventral visual pathway. After decades in which neurophysiological studies of object representation in the monkey ventral pathway have focused exclusively on 2D shape, recent reports indicate a robust representation of 3D shape, although the nature of that representation remains completely unknown. We will address this issue using the same techniques we have recently applied to produce the first quantitative descriptions of complex 2D shape representation. We will combine dense, parametric exploration of 3D shape space with intensive computational analysis to test hypotheses about 3D shape coding dimensions, tuning functions, integration mechanisms, and population coding principles. The stimuli will be complex, smooth (spline-based), abstract, randomly generated 3D shapes. Successive generations of random shape stimuli will be determined with a genetic algorithm, using neural responses as feedback to guide sampling toward the most relevant regions of 3D shape space. The resulting data will be used to test hypotheses about coding dimensions relating to 2D boundary contours, 3D surface patches, and 3D medial axis shape, all described in terms of absolute and relative position, 2D and 3D orientation, 2D and 3D curvature, curvature orientation, and curvature derivative. We will test tuning functions ranging from simple Gaussians to complex manifolds describing highly specific part shapes. We will test a variety of mechanisms for integrating information across object parts, ranging from single-part tuning through multi-part tuning to holistic tuning for overall object shape. The hypotheses surviving from these individual cell analyses will then be tested at the population coding level.

描述（由申请人提供）：我们生活在由3D物体组成的世界中并与之互动的能力在很大程度上取决于我们对视觉形状感知的惊人能力。这就是视力对我们的健康、幸福和生存至关重要的原因。该项目的长期目标是通过发现灵长类动物腹侧视觉通路中复杂3D形状的神经代码来理解3D物体感知。经过几十年的神经生理学研究的对象表示在猴子腹侧通路已经完全集中在2D形状，最近的报告表明，一个强大的表示3D形状，虽然该表示的性质仍然完全未知。我们将使用我们最近应用于产生复杂2D形状表示的第一个定量描述的相同技术来解决这个问题。我们将结合联合收割机密集，三维形状空间的参数化探索与密集的计算分析，以测试有关三维形状编码维度，调整功能，整合机制和人口编码原则的假设。刺激将是复杂的，平滑的（基于样条），抽象的，随机生成的3D形状。连续几代的随机形状刺激将通过遗传算法来确定，使用神经反应作为反馈来引导采样朝向3D形状空间的最相关区域。所得数据将用于测试与2D边界轮廓、3D表面贴片和3D中轴形状相关的编码尺寸的假设，所有这些都在绝对和相对位置、2D和3D方向、2D和3D曲率、曲率方向和曲率导数方面进行了描述。我们将测试从简单的高斯函数到描述高度特定零件形状的复杂流形的调整函数。我们将测试用于跨对象部分集成信息的各种机制，范围从单个部分调优到多部分调优，再到整体对象形状的整体调优。然后将在群体编码水平上检验这些单个细胞分析中存活的假设。