Neural coding of complex 3D shape

描述(由申请人提供)：我们在由3D对象组成的世界中生活并与之互动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。这就是为什么视力对我们的健康、幸福和生存如此重要。这个项目的长期目标是通过发现灵长类腹侧视觉通路中复杂3D形状的神经编码来理解3D物体感知。几十年来，对猴子腹侧通路中物体表征的神经生理学研究一直集中在2D形状上，最近的报告表明，3D形状的表征是稳健的，尽管这种表征的性质仍然完全不清楚。我们将使用我们最近应用的相同技术来解决这个问题，以产生复杂2D形状表示的第一个定量描述。我们将结合密集的3D形状空间的参数探索和密集的计算分析来测试关于3D形状编码维度、调谐函数、整合机制和种群编码原理的假设。刺激将是复杂的、平滑的(基于样条线的)、抽象的、随机生成的3D形状。连续几代随机形状刺激将用遗传算法确定，使用神经反应作为反馈，引导采样朝着3D形状空间中最相关的区域进行。生成的数据将用于测试与2D边界轮廓、3D曲面片和3D中轴形状相关的编码尺寸的假设，所有这些都以绝对和相对位置、2D和3D方向、2D和3D曲率、曲率方向和曲率导数来描述。我们将测试调谐函数，范围从简单的高斯型到描述高度特定零件形状的复杂流形。我们将测试各种跨对象部分集成信息的机制，从单部分调优到多部分调优，再到整体对象形状调优。从这些单个细胞分析中幸存下来的假设将在群体编码水平上进行测试。

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.