Visual-motor integration in smooth pursuit eye movements

摘要 许多行为在贝叶斯框架中运行，其中操作由 当前的感官信息和过去的经验，或“前科”。当当前的感觉信息很弱时，它是 有利于让先行者引导行为。然而，先前经验的价值随着感官的减少而减少。 信息变得更强大。我们的目标是确定贝叶斯行为是如何从神经操作中产生的 巡回赛。顺畅追逐眼球运动是相对简单的感觉-运动行为的一个例子 以贝叶斯式的方式。流畅的追逐是一种视觉引导的自愿眼球运动，可以分开 分为两个主要组成部分：视觉驱动力和视觉运动增益。想到这两个部分就很吸引人了 从贝叶斯的角度来看：视觉驱动产生于中间时间视区(MT)，并代表 由感官数据导出的似然函数；视觉运动增益由平滑追踪区控制 前部眼场(FEFsem)，表示前一个。这两个概念及其神经实例化是 集成在追逐系统中，以驱动终极眼球运动。对这两种成分都进行了研究 独立，但我们的目标是研究他们的融合。桥背外侧核(DLPN)和核团 被盖网状脑桥(NRTP)既接受皮层追踪区的会聚输入，又有细胞 带有一系列视觉和视觉运动信号。因此，我们将从DLPN中的单个神经元和 清醒行为恒河猴NRTP研究视觉和视觉运动信号在追踪中的整合 系统。为了更好地理解视觉和视觉运动信号的整合与 类贝叶斯行为的出现，我们已经开发出一种行为范式，使我们能够快速适应 目标速度的前科。通过调整目标的统计量，可以严格控制先验知识的适应性 速度，我们可以通过调整视觉运动信号的强度来控制先验的表达。这 将揭示脑桥中视觉和视觉运动信号的整合如何作为状态的函数变化 感官信息的先验和强度。直接研究FEFsem在这些方面的整合中的作用 信号，我们将把我们的脑桥录音与FEFsem中多个单一单元的同时录音配对。 脑桥和FEFsem神经元反应之间的神经元-神经元相关性将揭示功能性 区域之间的连接，并朝着描述追逐电路如何作为一个系统工作的方向发展。通过 为了更好地理解追踪系统是如何整合先验和感官信息的，我们将 开发感觉-运动脑回路的一般原理。

Abstract Many behaviors operate in a Bayesian framework, where actions are guided by a complex interaction between current sensory information and past experience, or “priors”. When current sensory information is weak, it is advantageous to allow the prior to guide behavior. However, the value of prior experience lessens as sensory information strengthens. Our goal is to determine how Bayesian behavior arises from the operation of a neural circuit. Smooth pursuit eye movement is an example of a relatively simple sensory-motor behavior that operates in a Bayesian-like manner. Smooth pursuit is a visually-guided voluntary eye movement that can be separated into two primary components: visual drive and visuomotor gain. It is appealing to think of these two components from a Bayesian perspective: the visual drive arises from the middle temporal visual area (MT) and represents the likelihood function derived from sensory data; the visuomotor gain is controlled by the smooth pursuit region of the frontal eye field (FEFsem) and represents the prior. The two concepts and their neural instantiations are integrated within the pursuit system to drive the ultimate eye movement. Both components have been studied independently, but our goal is to study their integration. The dorsolateral pontine nucleus (DLPN) and the nucleus reticularis tegmenti pontis (NRTP) both receive convergent input from cortical areas of pursuit and have cells with a range of visual and visuomotor signals. Therefore, we will record from single neurons in the DLPN and NRTP of awake behaving rhesus monkeys to study the integration of visual and visuomotor signals in the pursuit system. To better understand the relationship between the integration of visual and visuomotor signals and the emergence of Bayesian-like behavior, we have developed a behavioral paradigm that allows us to rapidly adapt priors for target speed. We can control tightly the adaptation of the prior by adjusting the statistics of the target speed, and we can control the expression of the prior by adjusting the strength of the visual motion signals. This will reveal how the integration of visual and visuomotor signals in the pons changes as a function of the state of the prior and the strength of sensory information. To directly study the role of FEFsem in the integration of these signals, we will pair our pontine recordings with simultaneous recordings of multiple single units in the FEFsem. Neuron-neuron correlations between the responses of pontine and FEFsem neurons will reveal functional connectivity between the areas and move towards a description of how the pursuit circuit works as a system. By gaining a better understanding of how the pursuit system integrates priors and sensory information, we will develop general principles of sensory-motor brain circuits.