CRCNS - Higher-Level Neural Specialization for Natural Shape Statistics

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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.