Neural Computations Underlying Cancellation of the Vestibular Consequences of Voluntary Movement

项目概述：本研究项目有三个目标。首先，我们将建立神经 大脑估计和消除自发前庭（内耳）的能力的基础机制 平衡）输入。第二，我们将确定前庭小脑如何学会 适应预期和实际感觉输入之间关系的变化，以保持稳定的感知 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。 大脑区分自我产生的感觉刺激的能力（即，活动）与 意外的或外部产生的（即，被动）刺激对于确保知觉稳定性至关重要， 精确的运动控制。值得注意的是，在前庭系统中，接受传入输入的相同的中枢神经元 还通过前庭脊髓反射向运动中枢发送直接投射以控制平衡和姿势。 这种反射对于对意外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，因此，抑制这种情况是有利的。 在积极的自我运动过程中。在过去的二十年里，我们取得了巨大的进步， 识别大脑在何处区分不同的（即，主动的）和外传入的（即，被动） 前庭信号具体地说，虽然前庭传入神经的反应仍然强劲（和等效）， 无论刺激是主动的还是被动的，前庭神经元在下一个处理阶段都是如此。 细胞核对主动自我运动的反应明显较弱。此外，我们已经表明，这种抑制 仅在感觉反馈与基于运动指令的预期相匹配时发生（例如，在正常 活动）。在拟议的研究中，我们将解决几个尚未解决的基本问题 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， 在自我运动中。首先，目标1中的实验将研究大脑如何计算前庭信号。 消除信号，从早期感觉处理中消除主动产生的信号。我们预测 小脑皮层在计算预期和实际之间的不匹配方面起着至关重要的作用 前庭输入以计算消除信号。目标2将决定小脑如何学习解释 当实际和预期的感觉反馈之间的关系是 改变了这些实验将提供对基于误差的机制的深入了解，这些机制确保了 维持前庭传入抑制机制。最后，在目标3中，我们将确定 动机如何调节小脑介导的前庭传入抑制。综合起来，这些研究 将（1）确定前庭反射消除信号的来源，（2）促进我们对 小脑适应前庭输入的变化，以及（3）阐明神经元机制的基础 激励性影响可以利用重复抑制来优化性能。

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.