CRCNS - Higher-Level Neural Specialization for Natural Shape Statistics

长期目标：发现高级视觉皮层如何专门利用我们自然环境的形状统计。自然物体有一个非常特殊的统计结构，这是由我们世界中的条件所决定的：重力、光照、物理、生物、建筑和标准观察者的观点。视觉系统是高度专业化的，以利用自然形状统计数据，并专注于最有用和可用的形状信息。这种专业化有助于使生物视觉远远优于目前基于计算机的视觉系统上级。了解这种专门化将揭示人类物体视觉的神经机制，并可能为基于计算机的视觉识别提出新的策略。 具体目标：（1）使用基于高通量计算机的摄影图像分析来表征自然物体的形状统计，（2）测量从清醒猕猴的高级腹侧通路视觉皮层记录的神经元中相同定量形状测量的调谐分布，（3）比较所得图像统计和神经调谐分布，以发现水平视皮层适应自然图像结构。 公共卫生相关性：这种新颖的分析大脑专业化的自然形状统计提供了一种新的方法来理解人类物体视觉的神经机制。了解这些机制将阐明视觉物体感知受损的疾病状态，为设计基于计算机的视觉系统提出新的策略，以补偿物体视觉的丧失，并最终为设计和优化基于微电极的设备提供关键知识基础。为更高级别的视觉皮质提供假体感觉输入。

DESCRIPTION (provided by applicant): Long-term Objective: To discover how higher-level visual cortex is specialized to exploit the shape statistics of our natural environment. Natural objects have a very specific statistical structure imposed by conditions in our world: gravity, lighting, physics, biology, architecture, and standard observer viewpoints. The visual system is highly specialized to take advantage of natural shape statistics and focus on the most useful and available shape information. This specialization helps make biological vision far superior to current computer-based vision systems. Understanding this specialization will shed new light on neural mechanisms of human object vision and could suggest new strategies for computer-based visual recognition. Specific Aims: (1) Use high-throughput computer-based photographic image analysis to characterize shape statistics of natural objects, (2) Measure the distribution of tuning for the same quantitative shape measures in neurons recorded from high-level ventral pathway visual cortex of awake macaque monkeys, (3) Compare the resulting image statistics and neural tuning distributions in order to discover how high-level visual cortex is adapted to natural image structure. Public Health Relevance: This novel analysis of brain specialization for natural shape statistics provides a fresh approach to understanding the neural mechanisms of human object vision. Understanding these mechanisms will elucidate disease states in which visual object perception is compromised, suggest new strategies for designing computer-based vision systems that could compensate for loss of object vision, and could ultimately provide the critical knowledge base for designing and optimizing microelectrode-based devices to provide prosthetic sensory inputs to higher-level visual cortex.