Activity-Dependent Tagging of Cerebellar Neurons for Studying Signal Processing and Learning

项目概要 拟议研究的目标是实施最先进的记录和操作技术 基于小脑中神经元的活动，剖析小脑执行的计算 控制眼球运动。视觉是一种主动的感觉，眼球运动的精确控制起着至关重要的作用。 在视觉中的作用。小脑在控制眼球运动和细化眼睛方面起着关键作用 通过动眼神经学习实现运动的准确性和精确性。众所周知，小脑的一部分 控制眼球运动接收视觉和前庭感觉信息以及眼睛的副本 运动命令，并可能使用这些感觉和运动信号来指导动眼神经性能 并通过学习对其进行修改。然而，许多技术挑战限制了我们研究如何 不同的感觉和运动信号在小脑中整合，以及不同的信号通路如何整合 在学习过程中都进行了修改，以提高动眼能力。两个新开发的工具，CaMPARI 和 Cal-Light，有望克服一些研究有限的技术挑战 小脑计算。这些工具在记录和操纵神经元的能力方面提供了先进的精度 基于他们在特定任务条件下的活动。我们将 (1) 评估 Campari 和 Cal-light 的功效 用于选择性地靶向（“标记”）任务和活动依赖性的小脑神经元亚群 方式，以及（2）使用这些工具来剖析小脑在动眼神经期间实现的计算 表现和动眼技能学习。拟议工作的技术成果将是一套新的 研究小脑的实验方法以及新的实验策略 其他神经回路中的计算和学习。科学成果将是关于 小脑对其感觉（视觉和前庭）和运动（参考副本）进行的计算 输入。了解小脑如何支持准确的眼球运动的进展将提供 为动眼神经疾病制定更合理的干预措施的概念基础，以及更多 通常用于与小脑功能障碍相关的多种疾病。

PROJECT SUMMARY The goal of the proposed research is to implement state-of-the-art techniques for recording and manipulating neurons based on their activity in the cerebellum, to dissect the computations performed by the cerebellum to control eye movements. Vision is an active sense, and the accurate control of eye movements plays an essential role in vision. The cerebellum plays a key role in the control of eye movements, and in the refinement of eye movement accuracy and precision through oculomotor learning. It is known that the part of the cerebellum controlling eye movements receives visual and vestibular sensory information as well as copies of the eye movement commands, and presumably uses these sensory and motor signals to guide oculomotor performance and its modification by learning. However, a number of technical challenges have limited our ability to study how different sensory and motor signals are integrated in the cerebellum, and how the different signaling pathways are each modified during learning to improve oculomotor performance. Two newly developed tools, CaMPARI and Cal-Light, hold great promise to overcome some of the technical challenges that have limited studies of cerebellar computation. These tools offer advanced precision in the ability to record and manipulate neurons based on their activity during specific task conditions. We will (1) evaluate the efficacy of CAMPARI and Cal-light for selectively targeting (“tagging”), subpopulations of cerebellar neurons in a task- and activity-dependent manner, and (2) use these tools to dissect the computations implemented by the cerebellum during oculomotor performance and oculomotor skill learning. The technical outcome of the proposed work will be a new set of experimental approaches for studying the cerebellum, as well as new experimental strategies for studying computation and learning in other neural circuits. The scientific outcome will be new insights about the computations performed by the cerebellum on its sensory (visual and vestibular) and motor (efference copy) inputs. Advances in understanding how the cerebellum supports accurate eye movements will provide conceptual underpinning for developing more rational interventions for oculomotor disorders, and, more generally for the wide array of disorders associated with cerebellar dysfunction.