title: A modular neural model of motor synergies.
abstract: Animals such as reptiles, amphibians and mammals (including humans) are mechanically extremely complex. It has been estimated that the human body has between 500 and 1400 degrees of freedom! And yet, these animals can generate an infinite variety of very precise, complicated and goal-directed movements in continuously changing and uncertain environments. Understanding how this is achieved is of great interest to both biologists and engineers. There are essentially two questions that must be addressed: (1) What type of control strategy is used to handle the large number of degrees of freedom involved? and (2) How is this strategy instantiated in the substrate of neural and musculoskeletal elements comprising the animal bodies? The first question has been studied intensively for several decades, providing strong indications 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 represent coordinated activity patterns over groups of muscles, and can be triggered as a whole with controlled amplitude and temporal offset. Complex movements can thus be constructed from the appropriate combination of a relatively small number of synergies, greatly simplifying the control problem. Although experimental studies on animal movements have confirmed the existence of motor synergies, and their utility has been demonstrated in the control of fairly complex robots, their neural basis remains poorly understood. In this paper, we introduce a simple but plausible and general neural model for motor synergies based on the principle that these functional modules reflect the structural modularity of the underlying physical system. Using this model, we show that a small set of synergies selected through a redundancy-reduction principle can generate a rich motor repertoire in a model two-jointed arm system. We investigate the synergies generated by this model systematically with respect to various parameters, and compare them to those observed in experiments.
authors.affiliation: School of Electronic and Computing Systems, University of Cincinnati, Cincinnati, OH 45221, USA.
authors.forename: Kiran V
authors.initials: KV
authors.lastname: Byadarhaly
authors.forename: Mithun C
authors.initials: MC
authors.lastname: Perdoor
authors.forename: Ali A
authors.initials: AA
authors.lastname: Minai
mesh_terms: D000222:Adaptation, Physiological; D000465:Algorithms; D000818:Animals; D001132:Arm / Q000294:innervation / Q000502:physiology; D005246:Feedback; D006801:Humans; D007596:Joints / Q000502:physiology; D008959:Models, Neurological; D009068:Movement / Q000502:physiology*; D018482:Muscle, Skeletal / Q000294:innervation / Q000502:physiology; D009142:Musculoskeletal Physiological Phenomena; D009424:Nervous System Physiological Phenomena; D016571:Neural Networks, Computer; D012371:Robotics; D012782:Shoulder / Q000502:physiology; D013116:Spinal Cord / Q000502:physiology