The motor system can be considered at three levels: motor behaviour, limb mechanics and neural control. Although our understanding at each level continues to grow, linking these levels into a cohesive framework is an important challenge for neuroscientists.
The peripheral motor system is a complex filter that converts patterns of muscle activity into movements. This conversion depends on the properties of muscle, multi-joint mechanics with uncoupled joint motion and muscular torque, as well as environmental forces acting on the limbs.
Despite these complexities, body movements are smooth and graceful. For example, hand trajectories during reaching movements are fairly straight, and hand velocity follows a smooth, bell-shaped profile. The motor system compensates for the complexities of limb mechanics, and also can adapt to perceptual or mechanical perturbations. There is considerable noise in the motor system, which is reflected in trial-to-trial variability, but task-relevant features are less variable.
Motor control involves the spinal cord, brainstem and cerebral cortex. Neurophysiological studies of motor control often involve recording the activity of neurons in different brain regions and relating such activity to aspects of sensorimotor function.
Such recordings indicate that neural activity in primary motor cortex (M1) correlates with hand direction, speed and distance of movement. However, theoretical studies have shown that such correlations can arise even if neurons code other details such as muscle activity or joint motion. There is also evidence that M1 behaves like or forms part of an inverse internal model that converts spatial goals or trajectories into detailed motor patterns.
Optimal feedback control might provide a link between the different levels of motor control. An optimal feedback controller uses an optimal estimate of the state of the system, generated through sensory feedback and efferent copy, and uses this feedback to adjust its output towards a specific goal. 'Errors' are corrected only if they adversely affect motor performance.
This control theory is consistent with a number of features of motor behaviour and of neural processing in M1. For example, the activity of M1 neurons varies depending on the behavioural context, and M1 receives a rich mix of sensory inputs.
Although the mathematics to compute an optimal feedback controller are quite complex, such controllers provide an important bridge to link limb mechanics, motor behaviour and the neural basis of motor control.As a theory, optimal feedback control generates a number of neurophysiological questions on how such a controller is created by the highly distributed circuitry involved in sensorimotor function.
Skilled motor behaviour, from the graceful leap of a ballerina to a precise pitch by a baseball player, appears effortless but reflects an intimate interaction between the complex mechanical properties of the body and control by a highly distributed circuit in the CNS. An important challenge for understanding motor function is to connect these three levels of the motor system — motor behaviour, limb mechanics and neural control. Optimal feedback control theory might provide the important link across these levels of the motor system and help to unravel how the primary motor cortex and other regions of the brain plan and control movement.
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This research was supported by research grants from the Canadian Institutes of Health Research and the Natural Sciences and Engineering Research Council of Canada. I am also supported by a CIHR Investigator Award. I would also like to acknowledge expert technical support from K. Moore and J. Swaine, as well as constructive criticisms on this manuscript from P. K. Rose, D. P. Munoz, I. Kurtzer, T. Herter and other members of the CIHR Group in Sensorimotor Research.
S.S. holds a US patent for a robotic device that is used to study limb motor function.
Encyclopedia of Life Sciences
motor neurons and spinal control of movement
motor output from the brain and spinal cord
proprioceptive sensory feedback
- MUSCULAR TORQUE
(or moment). Each muscle generates force from muscle contraction (active) and elastic forces (passive). Muscular torque for a muscle equals its total force multiplied by its moment arm (the perpendicular distance between a muscle's line of action and the joint centre of rotation).
- EQUILIBRIUM POINT MODELS
A class of models that assume the CNS can control the equilibrium position established by the balance of force that is generated by the spring-like behaviour of muscle.
- STATE VARIABLES
Estimates of the position of the limb or forces acting within or on the limb (or their derivative). State variables are transformed by corresponding feedback gains to generate motor output commands.
- ALPHA MOTOR NEURON
Motor neurons that innervate extrafusal muscle fibres that generate force.
- GAMMA MOTOR NEURON
Motor neurons that innervate intrafusal muscle fibres associated with muscle spindles.
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Scott, S. Optimal feedback control and the neural basis of volitional motor control. Nat Rev Neurosci 5, 532–545 (2004). https://doi.org/10.1038/nrn1427
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