RP 1: Modeling Forelimb Motor Circuit Organization and Function

项目总结：项目1-建立前肢电机电路组织和功能模型 这项研究项目致力于颈髓运动回路的动力学研究。本项目的目标 将分析颈椎网络在节律性和非节律性时如何协调前肢运动 活动。该模型将包括神经元元素(兴奋性和抑制性运动前神经元和拮抗剂 运动神经元池)、成对的肌肉和肢体的机械部件。理论上和 计算技术将被用来描述这些元素如何相互作用，包括对 下降的大脑指令，以产生前肢活动，如等长和等张运动。 第一个目标将是计算颈脊髓运动神经元如何对输入做出反应(基于切片 电生理学实验，项目2和3)，以及它们的激发如何转化为肌肉力量(基于 肌电记录，项目4)。第二个目标是分析实现最大控制的电路配置 以排序和预测可能存在于脊髓中的新的功能细胞类型。组成的网络模型 然后将构建运动和运动前神经元的结构，定义网络激发和爆发的模式 对抗运动池，对应于肘部和手腕的运动和静止状态。模型将是 受连接数据(项目2)的约束，并从最佳电路配置的理论分析中获得信息。 最后，肢体的机械模型将连接到神经元模型，以生成连贯的图像 从颈脊髓的下行输入到肢体运动的转换。《动力》 模型的反应将与收集的行为和活体电生理数据进行比较 项目4.总体而言，这里进行的理论分析以及来自 项目2-4将帮助了解有关多种神经元类型如何协调其反应的一般原则 性质以实现对多个动态变量的鲁棒控制。

Project Summary: Project 1 – Modeling Forelimb Motor Circuit Organization and Function This Research Project addresses the dynamics of motor circuits in the cervical spinal cord. Aims in this project will analyze how cervical spinal networks coordinate forelimb movement during rhythmic and non-rhythmic activity. The model will include neuronal elements (excitatory and inhibitory pre-motor neurons and antagonist pools of motor neurons), antagonist pairs of muscles, and mechanical components of the limb. Theoretical and computational techniques will be used to describe how these elements interact, including possible responses to descending brain commands, to generate forelimb activities such as isometric and isotonic movements. The first goal will be to compute how cervical spinal motor neurons fire in response to inputs (based on slice electrophysiology experiments, Projects 2 and 3), and how their firing is converted to muscle force (based on EMG recordings, Project 4). A second goal will be to analyze circuit configurations that achieve maximum control in order and predict new functional cell types that may be present in the spinal cord. Network models composed of motor and pre-motor neurons will then be constructed, defining the patterns of network firing and bursting of antagonistic motor pools, corresponding to elbow and wrist movements and static states. The model will be constrained by connectivity data (Project 2), and informed by theoretical analyses of optimal circuit configurations. Finally, a mechanical model of the limb will be connected to the neuronal model to generate a coherent picture of the transformation from descending input to the cervical spinal cord to limb movements. The dynamical responses of the model will be compared with the behavioral and in vivo electrophysiological data collected in Project 4. Overall, the theoretical analyses performed here, together with experimental investigations from Projects 2-4, will help inform general principles concerning how multiple neuronal types coordinate their response properties to achieve robust control of multiple dynamical variables.