Distributed representation of limb motor programs in arrays of adjustable pattern generators

Abstract This paper describes the current state of our exploration of how motor program concepts may be related to neural mechanisms. We have proposed a model of sensorimotor networks with architectures inspired by the anatomy and physiology of the cerebellum and its interconnections with the red nucleus and the motor cortex. We proposed the concept of rubrocerebellar and corticocerebellar information processing modules that function as adjustable pattern generators (APGs) capable of the storage, recall, and execution of motor programs. The APG array model described in this paper extends the single APG model of Houk et al. (1990) to an array of APGs whose collective activity controls movement of a simple two degree-of-freedom simulated limb. Our objective was to examine the APG array theory in a simple computational framework with a plausible relationship to anatomy and physiology. Results of simulation experiments show that the APG array model is capable of learning how to control movement of the simulated limb by adjusting distributed motor programs. Although the model is based on many simplifying assumptions, and the simulated motor control task is much simpler than an actual reaching task, these results suggest that the APG array model may provide a useful step toward a more comprehensive understanding of how neural mechanisms may generate motor programs.