  The motor control problem involves determining the time-varying muscle
activation trajectories required to accomplish a given movement. Muscle
redundancy makes motor control a challenging task: there are many possible
activation trajectories that accomplish the same movement. Despite this
redundancy, most movements are accomplished in highly stereotypical ways. For
example, point-to-point reaching movements are almost universally performed
with very similar smooth trajectories. Optimization methods are commonly used
to predict muscle forces for measured movements. However, these approaches
require computationally expensive simulations and are sensitive to the chosen
optimality criteria and regularization. In this work, we investigate deep
autoencoders for the prediction of muscle activation trajectories for
point-to-point reaching movements. We evaluate our DNN predictions with
simulated reaches and two methods to generate the muscle activations: inverse
dynamics (ID) and optimal control (OC) criteria. We also investigate optimal
network parameters and training criteria to improve the accuracy of the
predictions.
