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

• Main Authors: Hojeong Kim, Minjung Kim
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2018-04-01
• Series: Frontiers in Neuroinformatics
• Subjects: motoneuron; muscle fibers; motor unit; python; computer modeling; simulation software
• Online Access: http://journal.frontiersin.org/article/10.3389/fninf.2018.00015/full

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.