Motor Learning Tied to Intelligent Control of Sensory Neurons in the Muscles

Sensory neurons in human muscles provide important information used for the perception and control of movement. Learning to move in a novel context also relies on the brain’s independent control of these sensors, not just of muscles, according to a new study published in the journal Current Biology.

Each muscle can have tens or hundreds of encapsulated sensory receptors, and these “sensors” are called muscle spindles. Spindles differ from other sensory receptors as they also receive nerve fibers from the central nervous system itself, which acts to control spindle output.

There are more nerve fibers travelling to and from spindles than to the actual muscle tissues generating force and powering movement. Despite more than a hundred years of research on this class of sensory receptors, however, it has been unclear how, why and when the nervous system chooses to independently control spindles.

Image shows the recording of signals from nerve fibers.
Recording of signals from single nerve fibers during motor learning shows that signal patterns from sensory receptors in human muscle change substantially as a function of learning state. Credit: Michael Dimitriou.

“The findings strongly point to independent control of these sensors during motor learning,” says Dr. Michael Dimitriou, who conducted the study and is a researcher at the Department of Integrative Medical Biology at Umeå University in Sweden.

In this study, Dr. Dimitriou monitored spindle signals in humans while they learned to control the position of a visual cursor by moving their hand (much like using a computer mouse). Depending on what stage in the learning process, the spindles sent very different signals in response to virtually identical movements.

The research shows that the sensory capability of spindle neurons was adjusted according to the ongoing requirements of the task being learned. In other words, muscle spindle signal patterns were changed during the learning process to become selectively informative about different aspects of movement.

“It is well-known that effective extraction of information is a major component in good learning performance, and this is true in motor adaptation as well. Richer and more relevant sensory information from spindles allows for efficient update of the computational circuits in our brain that guide movement. Differing levels of skill in controlling muscle sensors is probably a factor defining individual differences in motor learning performance,” says Dr. Dimitriou.

Beyond increased understanding of how human motor learning works, the current findings may also have more practical implications, such as in prosthetic limb and robotics control, argues Michael Dimitriou:

“To use a common example, computer algorithms can easily defeat a human in a game of chess. However, even the most sophisticated robot cannot match the skill and dexterity of a child in moving pieces on the chessboard. Better understanding of human sensory control is a way forward.”

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Source: Daniel Harju – Umea University
Image Credit: The image is credited to Michael Dimitriou.
Original Research: Abstract for “Enhanced Muscle Afferent Signals during Motor Learning in Humans” by Michael Dimitriou in Current Biology. Published online March 31 2016 doi:10.1016/j.cub.2016.02.030


Abstract

Enhanced Muscle Afferent Signals during Motor Learning in Humans

Highlights
•Muscle spindle signals are modulated substantially during visuomotor learning
•Spindle control is independent from muscle activity but specific to learning state
•Increased spindle responses to stretch inhibit reflex antagonist muscle activity
•Control in sensory realignment (“washout”) tunes spindle signals to hand position

Summary
Much has been revealed concerning human motor learning at the behavioral level, but less is known about changes in the involved neural circuits and signals. By examining muscle spindle responses during a classic visuomotor adaptation task performed by fully alert humans, I found substantial modulation of sensory afferent signals as a function of adaptation state. Specifically, spindle control was independent of concurrent muscle activity but was specific to movement direction (representing muscle lengthening versus shortening) and to different stages of learning. Increased spindle afferent responses to muscle stretch occurring early during learning reflected individual error size and were negatively related to subsequent antagonist activity (i.e., 60–80 ms thereafter). Relative increases in tonic afferent output early during learning were predictive of the subjects’ adaptation rate. I also found that independent spindle control during sensory realignment (the “washout” stage) induced afferent signal “linearization” with respect to muscle length (i.e., signals were more tuned to hand position). The results demonstrate for the first time that motor learning also involves independent and state-related modulation of sensory mechanoreceptor signals. The current findings suggest that adaptive motor performance also relies on the independent control of sensors, not just of muscles. I propose that the “γ” motor system innervating spindles acts to facilitate the acquisition and extraction of task-relevant information at the early stages of sensorimotor adaptation. This designates a more active and targeted role for the human proprioceptive system during motor learning.

“Enhanced Muscle Afferent Signals during Motor Learning in Humans” by Michael Dimitriou in Current Biology. Published online March 31 2016 doi:10.1016/j.cub.2016.02.030

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