Cerebellar and basal ganglia contributions to motor learning

学习和改进新动作是我们日常生活的重要组成部分。我们通过观察先前的运动结果，并利用我们的适应性运动控制能力，成功地与环境中的物体进行交互。这一过程被认为涉及与动作选择/活力（基底神经节）和运动控制/学习（小脑）有关的大脑区域，它们在运动丘脑的水平上汇聚，以选择正确的动作类型并执行动作。尽管我们对相关的大脑区域有了深入的了解，但这些信号发出时间的重要性仍未得到解决。此外，这些信号在不同动作和学习周期之间的一致性也是未知的。在本工作计划中，我们将检验三个主要假设：1。小脑和基底神经节输入运动丘脑的时间随着任务的学习变得紧密相关。通过几种不同的行为，基底神经节向运动丘脑提供一致的信号，与每种行为的活力相关联。为了确定皮层下运动区域中运动学习的群体水平表征，我们将使用宽视场双光子群体钙成像和在执行Go/NoGo任务期间皮层下运动区域（基底神经节/运动丘脑）的神经元活动的电生理记录。基于病毒的光/化学遗传操作策略将用于调查活动和学习之间的因果关系。我们还将使用降维方法和贝叶斯解码器来探索运动区域神经元活动模式与学习之间的因果关系。通过应用先进的计算方法和计算机视觉算法，我们将深入了解皮层下运动区域的单细胞和网络动力学如何结合起来学习复杂的任务和生成准确的运动。

Learning and refinement of new movements is an essential component of our everyday lives. We use observation of previous movement outcomes and utilise our capacity for adaptable motor control to successfully interact with objects in our environment. This process is thought to involve brain areas concerned with action selection/vigour (basal ganglia) and motor control/learning (cerebellum), which converge at the level of the motor thalamus to select the correct action type and execute movements. Although we have an in-depth understanding of the brain areas involved, the importance of the timing of these signals remains unresolved. Furthermore, the consistency of these signals between different movements and learning periods is also unknown. In this programme of work, we will test three main hypotheses:1. Timing of cerebellar and basal ganglia input to motor thalamus becomes tightly correlated as a task becomes learned2. Across several different behaviours, basal ganglia provide a consistent signal to motor thalamus, correlating with the vigour of each behaviour3. Functional convergence of these inputs at the level of motor thalamus is important for the learning of a motor taskTo determine the population-level representations of motor learning in subcortical motor regions, we will use wide-field 2-photon population calcium imaging and electrophysiological recordings of neuronal activity in subcortical motor regions (basal ganglia / motor thalamus) during execution of a Go/NoGo task. Viral-based opto-/chemogenetic manipulation strategies will be used to investigate causality between activity and learning.We will also use dimensionality reduction methods and Bayesian decoders to explore causal links between neuronal activity patterns in motor regions and learning. By applying advanced computational methods and computer vision algorithms we will develop an in-depth understanding of how single cell and network dynamics in subcortical motor regions combine to learning complex tasks and generation of accurate movements.