Motor control

About: Motor control is a research topic. Over the lifetime, 14575 publications have been published within this topic receiving 433021 citations.

An Internal Model for Sensorimotor Integration

Abstract: On the basis of computational studies it has been proposed that the central nervous system internally simulates the dynamic behavior of the motor system in planning, control, and learning; the existence and use of such an internal model is still under debate. A sensorimotor integration task was investigated in which participants estimated the location of one of their hands at the end of movements made in the dark and under externally imposed forces. The temporal propagation of errors in this task was analyzed within the theoretical framework of optimal state estimation. These results provide direct support for the existence of an internal model.

Neuronal ensemble control of prosthetic devices by a human with tetraplegia

Abstract: Neuromotor prostheses (NMPs) aim to replace or restore lost motor functions in paralysed humans by routeing movement-related signals from the brain, around damaged parts of the nervous system, to external effectors. To translate preclinical results from intact animals to a clinically useful NMP, movement signals must persist in cortex after spinal cord injury and be engaged by movement intent when sensory inputs and limb movement are long absent. Furthermore, NMPs would require that intention-driven neuronal activity be converted into a control signal that enables useful tasks. Here we show initial results for a tetraplegic human (MN) using a pilot NMP. Neuronal ensemble activity recorded through a 96-microelectrode array implanted in primary motor cortex demonstrated that intended hand motion modulates cortical spiking patterns three years after spinal cord injury. Decoders were created, providing a ‘neural cursor’ with which MN opened simulated e-mail and operated devices such as a television, even while conversing. Furthermore, MN used neural control to open and close a prosthetic hand, and perform rudimentary actions with a multi-jointed robotic arm. These early results suggest that NMPs based upon intracortical neuronal ensemble spiking activity could provide a valuable new neurotechnology to restore independence for humans with paralysis. The cover shows Matt Nagle, first participant in the BrainGate pilot clinical trial. He is unable to move his arms or legs following cervical spinal cord injury. Researchers at the Department of Neuroscience at Brown University, working with biotech company Cyberkinetics and 3 other institutions, demonstrate that movement-related signals can be relayed from the brain via an implanted BrainGate chip, allowing the patient to drive a computer screen cursor and activate simple robotic devices. Such neuromotor prostheses could pave the way towards systems to replace or restore lost motor function in paralysed humans. Prior to this advance, this type of work has been performed chiefly in monkeys. The latest example of such research has achieved a large increase in speed over current devices, enhancing the prospects for clinically viable brain-machine interfaces.

Muscle and tendon: properties, models, scaling, and application to biomechanics and motor control

Abstract: Skeletal muscles transform neural control signals into forces that act upon the body segments to effect a coordinated motor task. This transformation is complex, not only because the properties of muscles are complex, but because the tendon affects the transmission of muscle force to the skeleton. This review focuses on how to synthesize basic properties of muscle and tendon to construct models applicable to studies of coordination. After a review of the properties of muscle and tendon, their integrated ability to generate force statically and dynamically is studied by formulating a generic model of the "musculotendon actuator", which has only one parameter, the ratio of tendon length at rest to muscle fiber length at rest. To illustrate the utility of the model, it is analyzed to show how this one parameter specifies whether excitation-contraction or musculotendon contraction is the rate-limiting process of force generation, whether elastic energy is stored in tendon or muscle, and whether hip- and knee-extensor actuators function as springs or dashpots during walking.

Optimal feedback control as a theory of motor coordination.

Abstract: A central problem in motor control is understanding how the many biomechanical degrees of freedom are coordinated to achieve a common goal. An especially puzzling aspect of coordination is that behavioral goals are achieved reliably and repeatedly with movements rarely reproducible in their detail. Existing theoretical frameworks emphasize either goal achievement or the richness of motor variability, but fail to reconcile the two. Here we propose an alternative theory based on stochastic optimal feedback control. We show that the optimal strategy in the face of uncertainty is to allow variability in redundant (task-irrelevant) dimensions. This strategy does not enforce a desired trajectory, but uses feedback more intelligently, correcting only those deviations that interfere with task goals. From this framework, task-constrained variability, goal-directed corrections, motor synergies, controlled parameters, simplifying rules and discrete coordination modes emerge naturally. We present experimental results from a range of motor tasks to support this theory.

Adaptive representation of dynamics during learning of a motor task

Abstract: We investigated how the CNS learns to control movements in different dynamical conditions, and how this learned behavior is represented. In particular, we considered the task of making reaching movements in the presence of externally imposed forces from a mechanical environment. This environment was a force field produced by a robot manipulandum, and the subjects made reaching movements while holding the end-effector of this manipulandum. Since the force field significantly changed the dynamics of the task, subjects' initial movements in the force field were grossly distorted compared to their movements in free space. However, with practice, hand trajectories in the force field converged to a path very similar to that observed in free space. This indicated that for reaching movements, there was a kinematic plan independent of dynamical conditions. The recovery of performance within the changed mechanical environment is motor adaptation. In order to investigate the mechanism underlying this adaptation, we considered the response to the sudden removal of the field after a training phase. The resulting trajectories, named aftereffects, were approximately mirror images of those that were observed when the subjects were initially exposed to the field. This suggested that the motor controller was gradually composing a model of the force field, a model that the nervous system used to predict and compensate for the forces imposed by the environment. In order to explore the structure of the model, we investigated whether adaptation to a force field, as presented in a small region, led to aftereffects in other regions of the workspace. We found that indeed there were aftereffects in workspace regions where no exposure to the field had taken place; that is, there was transfer beyond the boundary of the training data. This observation rules out the hypothesis that the subject's model of the force field was constructed as a narrow association between visited states and experienced forces; that is, adaptation was not via composition of a look-up table. In contrast, subjects modeled the force field by a combination of computational elements whose output was broadly tuned across the motor state space. These elements formed a model that extrapolated to outside the training region in a coordinate system similar to that of the joints and muscles rather than end-point forces. This geometric property suggests that the elements of the adaptive process represent dynamics of a motor task in terms of the intrinsic coordinate system of the sensors and actuators.