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PyMUS: Python-Based Simulation Software for Virtual Experiments on Motor Unit System

机译:PyMUS:基于Python的电机单元系统虚拟实验仿真软件

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We constructed a physiologically plausible computationally efficient model of a motor unit and developed simulation software that allows for integrative investigations of the input–output processing in the motor unit system. The model motor unit was first built by coupling the motoneuron model and muscle unit model to a simplified axon model. To build the motoneuron model, we used a recently reported two-compartment modeling approach that accurately captures the key cell-type-related electrical properties under both passive conditions (somatic input resistance, membrane time constant, and signal attenuation properties between the soma and the dendrites) and active conditions (rheobase current and afterhyperpolarization duration at the soma and plateau behavior at the dendrites). To construct the muscle unit, we used a recently developed muscle modeling approach that reflects the experimentally identified dependencies of muscle activation dynamics on isometric, isokinetic and dynamic variation in muscle length over a full range of stimulation frequencies. Then, we designed the simulation software based on the object-oriented programing paradigm and developed the software using open-source Python language to be fully operational using graphical user interfaces. Using the developed software, separate simulations could be performed for a single motoneuron, muscle unit and motor unit under a wide range of experimental input protocols, and a hierarchical analysis could be performed from a single channel to the entire system behavior. Our model motor unit and simulation software may represent efficient tools not only for researchers studying the neural control of force production from a cellular perspective but also for instructors and students in motor physiology classroom settings.
机译:我们构建了一个生理合理的电机单元计算效率模型,并开发了仿真软件,该软件可以对电机单元系统中的输入输出处理进行综合研究。首先通过将运动神经元模型和肌肉单元模型耦合到简化的轴突模型来构建模型运动单元。为了建立运动神经元模型,我们使用了最近报道的两室建模方法,该方法可在两种被动条件下准确捕获关键细胞类型相关的电学特性(体细胞输入电阻,膜时间常数以及体细胞与体细胞之间的信号衰减特性)。树突状细胞)和活跃状态(树突状体的流变电流和超极化持续时间以及树突状细胞的平台行为)。为了构造肌肉单元,我们使用了最近开发的肌肉建模方法,该方法反映了在整个刺激频率范围内,实验确定的肌肉激活动力学对肌肉长度的等距,等速运动和动态变化的依赖性。然后,我们基于面向对象的编程范例设计了仿真软件,并使用开源Python语言开发了该软件,使其可以使用图形用户界面完全运行。使用开发的软件,可以在广泛的实验输入协议下对单个运动神经元,肌肉单元和运动单元执行单独的仿真,并且可以从单个通道执行到整个系统行为的层次分析。我们的模型运动单元和仿真软件不仅可以为从细胞学角度研究力产生的神经控制的研究人员,而且可以为运动生理学教室环境中的讲师和学生提供有效的工具。

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