Are muscle synergies useful for neural control?

Aymar de Rugy, Gerald E. Loeb, Timothy J. Carroll
Front. Comput. Neurosci.. 2013-01-01; 7:
DOI: 10.3389/fncom.2013.00019

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1. Front Comput Neurosci. 2013 Mar 21;7:19. doi: 10.3389/fncom.2013.00019.
eCollection 2013.

Are muscle synergies useful for neural control?

de Rugy A(1), Loeb GE, Carroll TJ.

Author information:
(1)Centre for Sensorimotor Neuroscience, School of Human Movement Studies, The
University of Queensland Brisbane, QLD, Australia.

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-dimensional
modules 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.

DOI: 10.3389/fncom.2013.00019
PMCID: PMC3604633
PMID: 23519326

Auteurs Bordeaux Neurocampus