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（由申请人提供）： 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数据。自然物体具有由我们世界的条件所强加的非常具体的统计结构：重力、光照、物理、生物学、建筑和标准观察者视角。视觉系统高度专业化，可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当前基于计算机的视觉系统。了解这种专业性将为人类物体视觉的神经机制带来新的认识，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）使用基于计算机的高通量摄影图像分析来表征自然物体的形状统计，（2）测量从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清醒的猕猴，(3) 比较生成的图像统计数据和神经调节分布，以发现高级视觉皮层如何适应自然图像结构。 公共健康相关性：这种对自然形状统计的大脑专业化的新颖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物体感知受到损害的疾病状态，提出设计基于计算机的视觉系统的新策略，以补偿物体视觉的损失，并最终为设计和优化基于微电极的设备提供关键知识库为更高级别的视觉皮层提供假肢感觉输入。