LAUSR

There are numerous ways to reach for an apple hanging from a tree. Yet, our motor system uses a specific muscle activity pattern that features activity bursts and silent periods. We suggest that these bursts are an evidence against the common view that the brain controls the commands to the muscles in a smooth continuous manner. Instead, we propose a model in which a motor plan is transformed into a piecewise-constant control signal that is low-pass filtered and transmitted to the muscles with different muscle-specific delays. We use a Markov Chain Monte Carlo (MCMC) method to identify transitions in the state of the muscles following initial activation, and show that fitting a bang-bang control model to the kinematics of movement predicts these transitions in the state of the muscles. Such a bang-bang controller suggests that the brain reduces the complexity of the problem of ballistic movements control by sending commands to the muscles at sparse times. Identifying this bang-bang controller can be useful to develop efficient controllers for neuroprostheses and other physical human-robot interaction systems.