CRCNS - Higher-Level Neural Specialization/Natural Shape

CRCNS - 高级神经专业化/自然形状

• 批准号: 7047434
• 负责人: CHARLES E CONNOR
• 金额: $ 32.36万
• 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/7047434
• 关键词: Macaca mulatta; behavior test; behavioral /social science research tag; computational neuroscience; computer program /software; form /pattern perception; high throughput technology; image processing; microelectrodes; neural information processing; single cell analysis; statistics /biometry; substitute sight; visual cortex; visual perception

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.

描述(申请人提供)：长期目标：发现高级视觉皮质是如何专门利用我们自然环境的形状统计数据的。自然物体有一个由我们世界的条件强加的非常特定的统计结构：重力、照明、物理、生物学、建筑和标准的观察者观点。视觉系统高度专业化，以利用自然形状统计数据，并专注于最有用和最可用的形状信息。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。理解这种专门化将为人类物体视觉的神经机制提供新的线索，并可能为基于计算机的视觉识别提供新的策略。 具体目标：(1)使用高通量的基于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水平腹侧通路视觉皮质记录的神经元中相同数量形状测量的调谐分布；(3)比较由此产生的图像统计和神经调谐分布，以发现高级视觉皮质如何适应自然图像结构。 公共卫生相关性：这一对自然形状统计的大脑专门化的新分析为理解人类物体视觉的神经机制提供了一种新的方法。了解这些机制将阐明视觉对象知觉受损的疾病状态，为设计能够补偿对象视觉损失的基于计算机的视觉系统提供新的策略，并最终可能为设计和优化基于微电极的设备提供假体感觉输入到高级视觉皮质提供关键的知识基础。