Mechanisms of efficient coding of dynamic visual motion signals for pursuit

项目摘要：人眼向大脑发送信息的速度估计约为10% 兆比特每秒，大致相当于以太网连接的速度。处理如此大的带宽流 在行为相关时标上的视觉信息需要神经元提取和表征 有效地从视觉信号中获取信息，即以最少的时间和成本表示最多的信息 能量。从本质上讲，大脑需要像软件一样压缩视频流 电影的数字表现形式。人们对大脑如何完成这一关键任务知之甚少。我们 建议研究纹外视皮层用来编码运动信息的神经机制 在单个神经元、群体中，追逐眼动行为。MT区神经元的反应 可选择性地转换为视觉运动，并为顺畅的追逐眼球运动提供视觉输入。通过录制 神经和行为反应结合在一起，我们不仅可以确定皮质神经元如何压缩传入 有效地表示它们的视觉信号，以及这些编码策略是否对 行为表现。我们将在这个范例的基础上研究MT神经元如何联合编码运动 信息，由视网膜最近的工作指导，证明神经增强了刺激压缩 人口。拟议研究的总体目标是确定动态视觉运动刺激是如何 在大脑皮层神经元群体中的表现，以及该感觉信息随后被读取的效率 去引起追击。长期目标是确定大脑如何代表动态感官 信息，并对自然条件下的行为线索进行解码。这个项目可能会产生深远的影响 对我们理解大脑在自然条件下如何处理刺激以及我们如何 将感觉加工概念化。这项研究将有助于视网膜假体软件的开发， 将通过阐明大脑如何编码运动场景来弥补中央视觉处理的缺陷。 本研究的目的是研究(1)大脑皮质运动刺激的有效编码。 追逐。自适应感官编码通过调整灵敏度和积分时间来最大化信息传输 在当前的刺激条件下。我们将检验MT神经元自适应编码运动的假设，以及 当眼睛在飞行时，它们的编码效率会影响追踪性能。在自然动人的场景中， 运动的波动在许多时间尺度上是相关的。我们将在以下方面测量压缩效率 通过计算当前互信息实现自然运动的MT射击与追踪 回应和未来的刺激。我们的第二个目标(2)是量化动态MT种群编码 用于追捕的运动输入。我们将使用追踪的精度作为基准来约束大脑皮层的模型 种群编码，剖析尖峰和沉默模式的贡献--跨越时间和跨越 MT神经元群体--对目标运动进行编码，并测量编码池的大小。

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.!