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）：我们生活在内部并与由3D对象组成的世界相互作用的能力在很大程度上取决于我们壮观的视觉形状感知能力。这就是使视力对我们的健康，幸福和生存如此至关重要的原因。该项目的长期目标是通过在灵长类的腹侧视觉途径中发现复杂3D形状的神经代码来了解3D对象感知。几十年来，对猴子腹侧途径中对象表示的神经生理学研究仅集中在2D形状上，尽管该表示的性质仍然完全未知，但最近的报道表明3D形状的强大表示。我们将使用最近应用的相同技术来解决此问题，以产生复杂2D形状表示的第一个定量描述。我们将将3D形状空间的密集，参数探索与密集的计算分析相结合，以测试有关3D形状编码维度，调整功能，集成机制和人口编码原理的假设。刺激将是复杂的，光滑的（基于样条的），抽象的，随机生成的3D形状。连续的随机形状刺激将通过遗传算法确定，使用神经反应作为反馈，以指导采样到3D形状空间的最相关区域。所得数据将用于测试有关与2D边界轮廓，3D表面贴片和3D内侧轴形相关的编码维数的假设，这些假设均用绝对和相对位置，2D和3D方向，2D和3D曲率，曲率方向，曲率方向以及弯曲衍生物进行了描述。我们将测试从简单的高斯人到描述高度特定零件形状的复杂歧管的调谐功能。我们将测试各种机制，以整合跨对象零件的信息，从单部分调整到多部分调整到整体对象形状的整体调整。然后将在人群编码水平上测试这些单个细胞分析的假设。