Neural Computations Underlying Cancellation of the Vestibular Consequences of Voluntary Movement

项目概述：这项研究计划有三个目标。首先，我们将建立神经 大脑估计和消除自身产生的前庭(内耳)能力的基础机制 平衡)在主动运动中的输入。其次，我们将确定前庭小脑是如何学习 适应预期感官输入和实际感官输入之间关系的变化，以保持稳定的感知 和准确的行为。第三，我们将评估奖励-激励信号如何影响电路性能。 大脑辨别感觉刺激的能力，这些刺激是自身产生的(即，活跃的)还是 意想不到的或外部产生的(即被动的)刺激对于确保知觉稳定和 精确的电机控制。值得注意的是，在前庭系统中，接受传入输入的相同中枢神经元 也要通过前庭-脊髓反射将直接投射发送到运动中枢，以控制平衡和姿势。 这种反射对于对意想不到的前庭刺激提供强大的姿势反应是必不可少的，但它是相反的。 当目标是进行积极的头部运动时，效率很高。因此，抑制这一点是有利的 在主动自主运动中的路径。在过去的二十年里，我们在以下方面取得了出色的进展 识别大脑在哪里区分传入(即主动)和传出(即被动) 前庭信号。具体地说，当前庭传入反应保持强健(和等同)时 无论刺激是主动的还是被动的，前庭中处于下一处理阶段的神经元 核团对主动自我运动的反应明显较弱。此外，我们已经表明，这种压制 仅当感觉反馈与基于运动命令的预期匹配时才发生(例如，在正常期间 活跃的运动)。在拟议的研究中，我们将解决几个仍然悬而未决的基本问题 关于大脑执行的计算以确保稳定的感知和准确的运动控制 在自动运动过程中。首先，目标1的实验将研究大脑是如何计算前庭的 消除从早期感觉处理中主动产生的信号的消除信号。我们预测 小脑皮质在计算预期与实际之间的不匹配方面起着至关重要的作用 前庭输入以计算取消信号。目标2将决定小脑如何学习解释 当实际感觉反馈和预期感觉反馈之间的关系为 被更改了。这些实验将提供对基于错误的机制的洞察，这些机制确保校准 维持前庭再传入抑制机制。最后，在目标3中，我们将确定是否和 动机如何调节小脑介导的前庭再传入抑制。总而言之，这些研究 将(1)确定前庭再传入取消信号的来源，(2)促进我们对 小脑适应前庭输入的变化，以及(3)阐明神经机制是如何 再传入抑制可以被动机影响所利用，以优化绩效。

Project Summary: This research program is motivated by three goals. First, we will establish the neural mechanisms that underlie the brain's ability to estimate and cancel self-generated vestibular (inner ear balance) input during active movement. Second, we will determine how the vestibular cerebellum learns to adapt to changes in the relationship between expected and actual sensory input to maintain stabile perception and accurate behavior. Third, we will assess how reward-motivation signals influence circuit performance. The brain's ability to distinguish sensory stimuli that are the result of self-generated (i.e., active) versus unexpected or externally generated (i.e., passive) stimulation is vital to ensuring perceptual stability and accurate motor control. Notably, in the vestibular system, the same central neurons that receive afferent input also send direct projections to motor centers to control balance and posture via the vestibular-spinal reflex. This reflex is essential for providing robust postural responses to unexpected vestibular stimuli, yet is counter- productive when the goal is to make active head movements. Accordingly, it is advantageous to suppress this pathway during active self-motion. Over the past two decades, we have made excellent progress toward identifying where brain makes the distinction between reafferent (i.e., active) and exafferent (i.e., passive) vestibular signals. Specifically, while the responses of vestibular afferents remain robust (and equivalent) regardless of whether stimulation is active or passive, neurons at the next stage of processing in the vestibular nuclei are significantly less responsive to active self-motion. In addition, we have shown that this suppression only occurs when sensory feedback matches that expected based on the motor command (e.g., during normal active movements). In the proposed research, we will address several fundamental questions that remain open regarding the computations that the brain performs to ensure stable perception and accurate motor control during self-motion. First, experiments in Aim 1 will investigate how the brain computes the vestibular cancellation signal that eliminates actively generated signals from early sensory processing. We predict that the cerebellar cortex plays an essential role in computing the mismatch between expected and actual vestibular input to compute a cancellation signal. Aim 2 will determine how the cerebellum learns to interpret active motion as self-generated when the relationship between the actual and expected sensory feedback is altered. These experiments will provide insight into the error-based mechanisms that ensure calibration of the vestibular reafference suppression mechanism is maintained. Finally, in Aim 3 we will determine whether and how motivation modulates cerebellum-mediated vestibular reafference suppression. Combined, these studies will (1) determine the source of the vestibular reafference cancellation signal, (2) advance our understanding of the cerebellum adapts to changes in vestibular input, and (3) clarify how neuronal mechanisms underlying reafference suppression can be leveraged by motivational influences to optimize performance.