Synergistic organization of action: A computational model

Understanding the ability of humans and animals to exhibit a large repretoire of complex movements in a continuosly changing and uncertain environment is of interest to both biologists and engineers. Even the simplest movements require complex control of internal and external variables of the body and the environment in a variety of contexts. Classical methods - such as those used in industrial robotics - are difficult to apply in these high degree-of-freedom situations. Studies on motor control in animals have led to the discovery that, rather than using standard feedback control based on continuous tracking of desired trajectories, animals' movements emerge from the controlled combination of pre-configured movement primitives or synergies. These synergies define coordinated patterns of activity across specific sets of muscles, and can be triggered as a whole with controlled amplitude and temporal offset. Combinations of synergies, therefore, allow emergent configuration of a wide range of complex movements. Control is both simpler and richer in this synergistic framework because it is based on selection and combination of synergies rather than myopic tracking of trajectories. Though the existence of motor synergies is now well-established, there is very little computational modeling of them at the neural level. In this paper, we describe a simple neural model for motor synergies, and show how a small set of synergies selected through a redundancy-reduction principle can generate a rich motor repertoire in a model two-jointed arm system.