Mechanisms of efficient coding of dynamic visual motion signals for pursuit

追踪动态视觉运动信号的高效编码机制

• 批准号: 8632523
• 负责人: Leslie Carol Osborne
• 金额: $ 38.05万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-02-01 至 2019-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8632523
• 关键词: Accounting; Automobile Driving; Behavior; Behavioral; Benchmarking; Brain; Code; Computer software; Cues; Data; Development; Dimensions; Eye; Eye Movements; Future; Goals; Health; Human; Image; Lateral Geniculate Body; Life; Light; Measures; Modeling; Monkeys; Motion; Motor; Movement; Neurons; Noise; Pathway interactions; Pattern; Performance; Peripheral; Population; Process; Prosthesis; Proxy; Reading; Research; Retina; Retinal; Role; Rotation; Sensory; Sensory Process; Signal Transduction; Smooth Pursuit; Source; Speed; Stimulus; Stream; Structure; Testing; Time; Translating; Variant; Vision; Visual; Visual Cortex; Visual Motion; Visual system structure; Work; area MT; cost; digital; extracellular; extrastriate visual cortex; eye velocity; gaze; monocular; movie; neuromechanism; public health relevance; pursuit tracking; relating to nervous system; research study; response; simulation; software development; statistics; stimulus processing; tool; visual adaptation; visual information; visual motor; visual process; visual processing

PROJECT SUMMARY: The human eye sends information to the brain at an estimated rate of about 10 megabits per second, roughly the speed of an ethernet connection. Processing such a large bandwidth stream of visual information on behaviorally relevant time scales requires that neurons extract and represent information from visual signals efficiently, i.e. represent the most information for the least cost in time and energy. In essence, the brain needs to compress the visual stream much the same way software compresses the digital representation of a movie. Little is known about how the brain accomplishes this critical task. We propose to investigate the neural mechanisms that extrastriate visual cortex uses to encode motion information in single neurons, populations, and in pursuit eye movement behavior. Neurons in area MT respond selectively to visual motion and provide the visual inputs for smooth pursuit eye movements. By recording neural and behavioral responses together, we can determine not only how cortical neurons compress incoming visual signals to represent them efficiently but also whether those coding strategies are important for behavioral performance. We will build on that paradigm to study how MT neurons jointly encode motion information, guided by recent work in the retina demonstrating enhanced stimulus compression by neural populations. The general aim of the proposed research is to determine how dynamic visual motion stimuli are represented in a cortical neuronal population and how efficiently that sensory information is subsequently read out to generate pursuit. The long-term goal is to determine how the brain represents dynamic sensory information and decodes the cues for behavior under natural conditions. This project could have a profound impact on our understanding of how the brain processes stimuli under natural conditions and for how we conceptualize sensory processing. The study will aid the development of software for retinal prosthetics that will remediate deficits in central visual processing by elucidating how the brain encodes moving scenes. Our Aims are to study (1) the Efficient sensory coding of dynamic motion stimuli in cortical area MT and pursuit. An adaptive sensory code maximizes information transfer by adjusting sensitivity and integration time to the current stimulus conditions. We will test the hypothesis that MT neurons adaptively encode motion, and that their coding efficiency impacts pursuit performance while the eyes are in flight. In natural moving scenes, fluctuations in motion are correlated across many time scales. We will measure the compression efficiency in MT firing and pursuit tracking of naturalistic motion by computing the mutual information between present response and future stimulus. Our second Aim (2) is to Quantify dynamic MT population encoding of motion inputs for pursuit. We will use the precision of pursuit as a benchmark to constrain models of cortical population coding, dissecting the contribution of patterns of spikes and silences -- across time and across populations of MT neurons -- to the encoding of target motion and to measure the size of the coding pool.!

项目摘要：人眼向大脑发送信息的速度估计约为10次