Are muscle synergies useful for neural control?

de Rugy, Aymar, Loeb, Gerald E. and Carroll, Timothy J. (2013) Are muscle synergies useful for neural control?. Frontiers in Computational Neuroscience, 7 MAR: . doi:10.3389/fncom.2013.00019


Author de Rugy, Aymar
Loeb, Gerald E.
Carroll, Timothy J.
Title Are muscle synergies useful for neural control?
Journal name Frontiers in Computational Neuroscience   Check publisher's open access policy
ISSN 1662-5188
Publication date 2013-03-21
Year available 2013
Sub-type Article (original research)
DOI 10.3389/fncom.2013.00019
Open Access Status DOI
Volume 7
Issue MAR
Total pages 13
Place of publication Lausanne, Switzerland
Publisher Frontiers Research Foundation
Collection year 2014
Language eng
Abstract The observation that the activity of multiple muscles can be well approximated by a few linear synergies is viewed by some as a sign that such low-dimensionalmodules constitute a key component of the neural control system. Here, we argue that the usefulness of muscle synergies as a control principle should be evaluated in terms of errors produced not only in muscle space, but also in task space. We used data from a force-aiming task in two dimensions at the wrist, using an electromyograms (EMG)-driven virtual biomechanics technique that overcomes typical errors in predicting force from recorded EMG, to illustrate through simulation how synergy decomposition inevitably introduces substantial task space errors. Then, we computed the optimal pattern of muscle activation that minimizes summed-squared muscle activities, and demonstrated that synergy decomposition produced similar results on real and simulated data. We further assessed the influence of synergy decomposition on aiming errors (AEs) in a more redundant system, using the optimal muscle pattern computed for the elbow-joint complex (i.e., 13 muscles acting in two dimensions). Because EMG records are typically not available from all contributing muscles, we also explored reconstructions from incomplete sets of muscles. The redundancy of a given set of muscles had opposite effects on the goodness of muscle reconstruction and on task achievement; higher redundancy is associated with better EMG approximation (lower residuals), but with higher AEs. Finally, we showed that the number of synergies required to approximate the optimal muscle pattern for an arbitrary biomechanical system increases with task-space dimensionality, which indicates that the capacity of synergy decomposition to explain behavior depends critically on the scope of the original database. These results have implications regarding the viability of muscle synergy as a putative neural control mechanism, and also as a control algorithm to restore movements.
Keyword Aiming movement
Muscle coordination
Motor control
Biomechanics
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Article # 19

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Human Movement and Nutrition Sciences Publications
 
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