Simplified and effective motor control based on muscle synergies to exploit musculoskeletal dynamics

Abstract

The basic hypothesis of producing a range of behaviors using a small set of motor commands has been proposed in various forms to explain motor behaviors ranging from basic reflexes to complex voluntary movements. Yet many fundamental questions regarding this long-standing hypothesis remain unanswered. Indeed, given the prominent nonlinearities and high dimensionality inherent in the control of biological limbs, the basic feasibility of a lowdimensional controller and an underlying principle for its creation has remained elusive. We propose a principle for the design of such a controller, that it endeavors to control the natural dynamics of the limb, taking into account the nature of the task being performed. Using this principle, we obtained a low-dimensional model of the hindlimb and a set of muscle synergies to command it. We demonstrate that this set of synergies was capable of producing effective control, establishing the viability of this muscle synergy hypothesis. Finally, by combining the low-dimensional model and the muscle synergies we were able to build a relatively simple controller whose overall performance was close to that of the system's full-dimensional nonlinear controller. Taken together, the results of this study establish that a low-dimensional controller is capable of simplifying control without degrading performance.low-dimensional ͉ optimal control ͉ muscle pattern ͉ frog ͉ computational model C ontrolling any movement, whether it be a stereotyped reflex or a sophisticated skill, is highly complex. Fundamentally, every movement requires the detailed specification of a vast number of variables, potentially involving many thousands of motor units distributed throughout the limbs and body. Further, the relationship between these variables and the intended motion of the body is nontrivial, dictated by the intricate nonlinear dynamics of the musculoskeletal system. Elucidating control strategies that can overcome these complexities is a central issue in the neural control of movement.Many investigators have suggested that the central nervous system (CNS) might have developed strategies to simplify the control of movement (1-6). According to one common proposal, the CNS might produce movement through the flexible combination of ''muscle synergies,'' with each such synergy specifying a particular balance of activation across a set of muscles (7-16). By reducing the number of controlled variables, such a lowdimensional control strategy would simplify the production of movement.Although many experiments have found evidence to suggest that many behaviors can be produced through combinations of muscle synergies, several questions concerning this hypothesis remain unresolved. Foremost among these questions is a proof of the concept's viability: can a low-dimensional control scheme based on muscle synergies reproduce the range of observed behaviors with negligible loss of efficacy? Given the nonlinearities and high dimensionality inherent in biological motor control, the answer to this question is not ...

Differences in Adaptation Rates after Virtual Surgeries Provide Direct Evidence for Modularity

“…A modular organization may allow the CNS to rapidly acquire and efficiently control motor skills, overcoming the complexity inherent in the coordination of the many degrees of freedom of the musculoskeletal system (Bernstein, 1967). Adequate, yet possibly suboptimal, control policies for generating a muscle activation pattern driving an end effector onto a visual target, as in our task, and, in general, for accomplishing a variety of different goals may be constructed by combining a small number of modules (Berniker et al, 2009;McKay and Ting, 2012). Modules such as muscle synergies may capture regularities in the sensorimotor mappings shared across tasks and conditions, reducing the number of parameters to be selected to generate a motor command and to be adjusted to compensate for a perturbation or to acquire a new skill.…”

“…Direct evidence for modularity would come from testing an experimental manipulation that can distinguish a modular controller from a non-modular one (d'Avella et al, 2008;Tresch and Jarc, 2009;d'Avella and Pai, 2010). Here we tested a manipulation of the mapping between muscle activations and hand forces that could make such a distinction.…”

Identification of synergies by optimization of trajectory tracking tasks

“…In line with the analytical procedures [7], [11], our method gives a clear mathematical interpretation of muscle synergies; however, it does not require the dynamical model of the system in analytical form. Moreover, unlike [11], [12], our synergies are optimised to perform trajectory tracking, rather then reaching tasks.…”

“…Other researchers approximate the dynamics of the system with a lower order linear dynamical model, and obtain a set of primitives by algebraic considerations on its matrices [11]. Another approach consists of applying a learning procedure to a training set of sensory-motor data generated by actuating the robot with random pulses [8], [12]. Although it bypasses the limitations of the analytical formulation, this method does not provide a formal definition of the extracted synergies.…”

The neural optimal control hierarchy for motor control

“…The cerebellum is widely regarded as an adaptation device, performing online error correction, and is thus often taken as the site for the storage of internal models [21,7,93,48]. In the NOCH, the cerebellum is taken to play a similar role.…”