N. Berthier, S. P. Singh, A. G. Barto, J. C. Houk
A neurophysiologically-based model is presented that controls a simulated kinematic arm during goal-directed reaches. The network generates a quasi-feedforward motor command that is learned using training signals generated by corrective movements. For each target, the network selects and sets the output of a subset of pattern generators. During the move(cid:173) ment, feedback from proprioceptors turns off the pattern generators. The task facing individual pattern generators is to recognize when the arm reaches the target and to turn off. A distributed representation of the mo(cid:173) tor command that resembles population vectors seen in vivo was produced naturally by these simulations.