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首页> 外文期刊>Journal of Neurophysiology >A model of the rat medial gastrocnemius muscle based on inputs to motoneurons and on an algorithm for prediction of the motor unit force
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A model of the rat medial gastrocnemius muscle based on inputs to motoneurons and on an algorithm for prediction of the motor unit force

机译:基于电动元素输入的大鼠内侧腓肠肌肌肉模型及电动机单元力预测算法

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摘要

The muscle force is the sum of forces of multiple motor units (MUs), which have different contractile properties. During movements, MUs develop unfused tetani, which result from summation of twitch-shape responses to individual stimuli, which are variable in amplitude and duration. The aim of the study was to develop a realistic muscle model that would integrate previously developed models of MU contractions and an algorithm for the prediction of tetanic forces. The proposed model of rat medial gastrocnemius muscle is based on physiological data: excitability and tiring frequencies of motoneurons, contractile properties, and the number and proportion of MUs in the muscle. The MU twitches were modeled by a six-parameter analytical function. The excitability of motoneurons was modeled according to a distribution of their rheo-base currents measured experimentally. Processes of muscle force regulation were modeled according to a common drive hypothesis. The excitation signal to motoneurons was modeled by two form types: triangular and trapezoid. The discharge frequencies of MUs, calculated individually for each MU, corresponded to those recorded for rhythmic firing of motoneurons. The force of the muscle was calculated as the sum of all recruited MUs. Participation of the three types of MUs in the developed muscle force was presented at different levels of the excitation signal to motoneurons. The model appears highly realistic and open for input data from various skeletal muscles with different compositions of MU types. The results were compared with three other models with different distribution of the input parameters.
机译:肌肉力是多种电机单元(MU)的力之和,其具有不同的收缩性能。在运动期间,MUS开发出不利用的Tetani,这导致了对单个刺激的抽搐形状响应的总和,这在幅度和持续时间内变化。该研究的目的是开发一种现实的肌肉模型,该模型将结合以前开发的MU凹陷模型和用于预测滴答力的算法。提出的大鼠内侧胃肠肌肌肌肌肉是基于生理数据:运动神经元,收缩性质和肌肉中亩的数量和比例的兴奋性和累赘。 Mu Tuches由六参数分析功能进行建模。根据实验测量的Rheo-Base电流的分布,模拟了运动神经元的兴奋性。根据共同的驱动假设,对肌肉力调节的过程进行了建模。对运动神经元的激发信号由两种形式的类型进行建模:三角形和梯形。对每亩的单独计算的MUS的放电频率对应于记录的那些用于运动神经元的节奏射击。肌肉的力量被计算为所有招募的肌肉的总和。在发达的肌肉力中的三种类型的肌肉中的参与在不同水平的激发信号中呈现给运动神经元。该模型显得高度逼真和开放,用于来自各种骨骼肌的输入数据,具有不同的MU类型的组成。将结果与三种其他模型进行比较,具有不同分布的输入参数。

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