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: 10557073
• 负责人: JEFFREY M BECK
• 金额: $ 13.42万
• 依托单位: DUKE UNIVERSITY
• 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 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10557073
• 关键词: Address; Anatomy; Area; Automobile Driving; Behavior; Bite; Brain; Brain Diseases; Cell Nucleus; Code; Communication; Data; Development; Diagnosis; Differential Diagnosis; Dimensions; Disease; Eye; Eye Movements; Goals; Grant; Injury; Knowledge; Maps; Measurement; Measures; Methods; Microelectrodes; Modeling; Monitor; Monkeys; Motion; Motor; Movement; Neurodegenerative Disorders; Neurons; Noise; Output; Pathway interactions; Performance; Play; Pontine structure; Population; Population Analysis; Primates; Prosthesis; Proxy; Reaction; Retina; Role; Rotation; Sampling; Sensory; Signal Transduction; Smooth Pursuit; Speed; Stimulus; Study models; System; Testing; Time; Translating; Variant; Visual; Visual Cortex; Visual Motion; Work; area MT; behavioral response; density; developmental disease; extrastriate visual cortex; eye velocity; improved; information model; information processing; neural circuit; oculomotor; optic flow; orbit muscle; population based; research clinical testing; response; retinal imaging; sensorimotor system; sensory cortex; sensory stimulus; stroke patient; synergism; theories; transmission process; vector; visual coding; visual motor

Abstract Current theories of how the brain estimates stimuli from sensory populations are based on non-adapting responses to single-parameter, constant stimuli over long time windows – highly unnatural conditions. To understand sensory circuits we need to determine how dynamic, multi-dimensional stimuli are decoded for rapid behaviors by adapting neurons. Progress may depend less on our ability to record ever-larger samples of cortical activity, and more on our ability to relate the activity we observe to the brain's read out. In short, we need a proxy for the answer that we are trying to model from our recordings – a precise behavioral response. We propose to exploit the close connection between cortical visual motion representation and pursuit eye movements in monkeys to study two significant problems: (1) how cortico-pontine projections transform the distributed place code for visual signals in cortex to a form better suited to driving motor areas and (2) the how the brain forms stable sensory estimates with adapting neurons. Our focus is how activity in area MT is transformed by downstream projections to the dorsolateral pontine nucleus (DLPN). DLPN transmits estimates of retinal motion to the flocculus to initiate and maintain pursuit, although other pathways also contribute. The pursuit system is an excellent model for studying sensory decoding because little noise is added in downstream motor processing- - the eye movement is a faithful rendering of the brain's estimate of target motion. Although the eye pursues correctly, we showed that MT neurons do not maintain a fixed relationship between firing rate and retinal motion. Our first aim is to determine if the MT-DLPN projection reduces noise, filters out talk irrelevant signals, and alters the coordinate from direction-speed to the H-V axes of the extra-ocular muscles. We propose to record from MT and DLPN in behaving monkeys, using tetrodes to record groups of nearby neurons and eye coils to monitor eye movements very accurately. Our second aim is to determine how the brain recovers veridical stimulus estimates from an adapting sensory population. Adaptation is ubiquitous in the brain, often driven by rapid changes in natural stimuli. In the previous grant period, we showed that MT neurons adapt their gain to the direction variance of a dynamic motion stimulus, making good use of a limited response bandwidth. Gain adaptation increases bit rates but it also creates ambiguity because the mapping between motion direction and firing rate is not fixed. In our second aim, we will investigate whether a downstream area needs information about the stimulus variance to properly estimate motion direction. Pursuit behavior shows that the brain solves this problem, but gain adaptation seems to foil our current decoding models. We will use information-based methods applied to MT and DLPN data to determine how to form a successful read-out. Our proposed work will create more realistic theories of sensory coding and knowledge of the brain's mechanisms for implementing them.

摘要 目前关于大脑如何估计感官群体刺激的理论是基于非适应性的 在长时间窗口内对单参数恒定刺激的反应-高度不自然的条件。到 了解感觉电路，我们需要确定如何动态，多维刺激解码，以快速 通过调整神经元来改变行为。研究进展可能不太依赖于我们记录更大的大脑皮层样本的能力， 活动，以及我们将我们观察到的活动与大脑的读出联系起来的能力。简而言之，我们需要一个代理人 我们试图从我们的记录中找到答案-一种精确的行为反应。我们建议 利用皮质视觉运动表征和追踪眼球运动之间的密切联系， 猴研究两个重要问题：（1）皮质-脑桥投射如何改变分布的地方 大脑皮层中视觉信号的编码转换为更适合驱动运动区域的形式，以及（2）大脑如何形成 稳定的感觉估计与适应神经元。我们的重点是MT区域的活动如何被 向脑桥背外侧核（DLPN）的下游投射。DLPN传输视网膜运动的估计 到绒球来发起和维持追击，尽管其他途径也有贡献。追踪系统是 这是研究感觉解码的一个很好的模型，因为在下游的运动处理中几乎没有噪音- - 眼球运动是大脑对目标运动估计的忠实表现。虽然眼睛追求 正确地，我们发现MT神经元在放电率和视网膜运动之间不保持固定的关系。 我们的第一个目标是确定MT-DLPN投影是否降低了噪声，过滤掉与谈话无关的信号，并改变 从方向-速度到眼外肌H-V轴的坐标。我们建议从MT记录 和DLPN在行为猴子，使用四极记录附近的神经元和眼线圈组，以监测眼睛 动作非常准确。我们的第二个目标是确定大脑如何恢复真实的刺激估计 从一个适应性的感官群体。适应在大脑中无处不在，通常是由大脑的快速变化驱动的。 自然刺激。在前一个授予期间，我们表明MT神经元使其增益适应方向方差 动态运动刺激，充分利用有限的响应带宽。增益自适应增加位 但是它也产生了模糊性，因为运动方向和发射速率之间的映射不是固定的。在 我们的第二个目标，我们将调查下游地区是否需要有关刺激方差的信息 以正确地估计运动方向。追求行为表明，大脑解决这个问题，但获得 适应似乎挫败了我们目前的解码模型。我们将使用基于信息的方法应用于机器翻译 和DLPN数据来确定如何形成成功的读出。我们提出的工作将创造更现实的 感觉编码的理论和大脑实现它们的机制。

项目成果

Spatiotemporal Filter for Visual Motion Integration from Pursuit Eye Movements in Humans and Monkeys

• DOI: 10.1523/jneurosci.2682-16.2016
• 发表时间: 2017-02-08
• 期刊: JOURNAL OF NEUROSCIENCE
• 影响因子: 5.3
• 作者: Mukherjee, Trishna;Liu, Bing;Osborne, Leslie C.
• 通讯作者: Osborne, Leslie C.

Shared sensory estimates for human motion perception and pursuit eye movements.

对人类运动感知和追踪眼球运动的共享感官估计。

• DOI: 10.1523/jneurosci.4320-14.2015
• 发表时间: 2015
• 期刊: The Journal of neuroscience : the official journal of the Society for Neuroscience
• 作者: Mukherjee,Trishna;Battifarano,Matthew;Simoncini,Claudio;Osborne,LeslieC
• 通讯作者: Osborne,LeslieC

Efficient sensory cortical coding optimizes pursuit eye movements.

• DOI: 10.1038/ncomms12759
• 发表时间: 2016-09-09
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Liu B;Macellaio MV;Osborne LC
• 通讯作者: Osborne LC