title: Modularity for Motor Control and Motor Learning.
abstract: How the central nervous system (CNS) overcomes the complexity of multi-joint and multi-muscle control and how it acquires or adapts motor skills are fundamental and open questions in neuroscience. A modular architecture may simplify control by embedding features of both the dynamic behavior of the musculoskeletal system and of the task into a small number of modules and by directly mapping task goals into module combination parameters. Several studies of the electromyographic (EMG) activity recorded from many muscles during the performance of different tasks have shown that motor commands are generated by the combination of a small number of muscle synergies, coordinated recruitment of groups of muscles with specific amplitude balances or activation waveforms, thus supporting a modular organization of motor control. Modularity may also help understanding motor learning. In a modular architecture, acquisition of a new motor skill or adaptation of an existing skill after a perturbation may occur at the level of modules or at the level of module combinations. As learning or adapting an existing skill through recombination of modules is likely faster than learning or adapting a skill by acquiring new modules, compatibility with the modules predicts learning difficulty. A recent study in which human subjects used myoelectric control to move a mass in a virtual environment has tested this prediction. By altering the mapping between recorded muscle activity and simulated force applied on the mass, as in a complex surgical rearrangement of the tendons, it has been possible to show that it is easier to adapt to a perturbation that is compatible with the muscle synergies used to generate hand force than to a similar but incompatible perturbation. This result provides direct support for a modular organization of motor control and motor learning.
authors.affiliation: Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy. andrea.davella@unime.it.|Laboratory of Neuromotor Physiology, Santa Lucia Foundation, Rome, Italy. andrea.davella@unime.it.
authors.forename: Andrea
authors.initials: A
authors.lastname: d'Avella
mesh_terms: D000222:Adaptation, Physiological / Q000502:physiology; D004576:Electromyography; D006801:Humans; D007858:Learning / Q000502:physiology*; D009048:Motor Skills / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D009142:Musculoskeletal Physiological Phenomena
keywords: Coordinated recruitment; Degrees-of-freedom (DOF); Electromyography (EMG); Inverse dynamcis; Inverse kinematics; Iterative algorithm; Joint angles trajectory; Muscle synergies