System identification of a motor unit pool using a realistic neuromusculoskeletal model

The synaptic input to the motoneurons cannot be measured in humans due to ethical and technical difficulties. For these reasons realistic computational models of a motoneuron pool and the innervated muscle fibers (a “motor unit pool”) have an important role in the study of the human control of muscles. However such models are complex and their mathematical analysis is difficult. We present a system identification approach of a realistic motor unit pool model with the objective of obtaining a simpler model capable of representing the transduction of the motoneuron pool inputs into the force generated by the respective muscle. The system identification was based on an orthogonal least squares algorithm to find a NARMAX model, the input being the net dendritic excitatory synaptic conductance of the motoneurons and the output being the force produced by the muscle. The identified model output reproduced the mean behavior of the output from the realistic computational model even for input-output signals not used in the identification process, such as sinusoidally modulated output force signals.