Dissecting circuit and cellular mechanisms for limb motor control

运动神经元与肌肉相连，构成神经系统的主要输出。模式 运动神经元中的神经活动引起时间上精确的肌肉收缩，产生协调的 灵活的行为。这些模式是由前运动回路的连通性和生理学塑造的 与运动神经元形成突触。预电机电路联合收割机下降电机 用感觉反馈信号来驱动运动神经元活动。前运动网络是如何 被构造成控制运动输出的结构还没有被很好地理解，部分原因是脊柱的不完整的库存。 细胞类型，以及记录行为动物神经活动的困难。为了弥补这一差距， 一个项目旨在使用果蝇作为研究运动控制和前运动的模型 神经回路随着一个可访问的和数字紧凑的神经系统，一个大的和不断增长的套件 基因工具和敏捷的四肢运动，果蝇有潜力提供洞察力， 电机控制的基本问题。我们引入了一个任务，其中苍蝇学习产生特定的 将股骨-胫骨关节定位在不同目标中的力的大小。这个关节由12个 这些神经元可以被基因标记用于靶向神经记录。电生理学记录 将揭示完整的运动神经元群体如何共同发挥作用，以动态定位运动神经元。 腿和维持给定的力输出。这些数据将解决长期存在的假设， 前运动回路招募运动神经元的子集，以及这种控制的灵活程度。然后， 一种新的电子显微镜水平的中枢运动回路重建将允许识别关键的 前运动神经元这些运动前神经元在行为任务中的电生理记录 将揭示它们对处理感觉反馈和控制腿部力量的贡献。这些结果 将为理解下行命令如何与内部模型交互提供基础。 身体状态来控制运动，这是实现设计长期目标的关键一步。 神经肌肉疾病的干预和控制工程系统的算法。

Motor neurons connect to muscles and comprise the major output of the nervous system. Patterns of neural activity in motor neurons cause temporally precise muscle contractions, producing coordinated and flexible behavior. These patterns are shaped by the connectivity and physiology of premotor circuits in the spinal cord that synapse onto the motor neurons. Premotor circuits combine descending motor commands with sensory feedback signals to drive motor neuron activity. How premotor networks are structured to control motor output is not well understood, due in part to an incomplete inventory of spinal cell types, and to the difficulties of recording neural activity in behaving animals. To address this gap, this project aims to use Drosophila melanogaster as a model for investigating motor control and premotor neural circuits. With an accessible and numerically compact nervous system, a large and growing suite of genetic tools, and agile, limbed locomotion, Drosophila has the potential to provide insight into fundamental problems of motor control. We introduce a task in which flies learn to generate specific amounts of force to position the femur-tibia joint in different targets. The joint is controlled by twelve neurons which can be genetically labeled for targeted neural recordings. Electrophysiological recordings will reveal how the complete population of motor neurons function together to dynamically position the leg and to sustain a given force output. These data will address long-standing hypotheses about how premotor circuits recruit subsets of motor neurons and the degree to which that control is flexible. Then, a new electron-microscopy level reconstruction of central locomotor circuits will allow identification of key premotor neurons. Electrophysiological recordings of those premotor neurons during the behavioral task will reveal their contributions to processing sensory feedback and to controlling leg force. These results will provide a foundation for understanding how descending commands interact with internal models of body state to control locomotion, a critical step toward achieving the long-term goals of designing interventions for neuromuscular disorders and algorithms for controlling engineered systems.