![]() ![]() There is now considerable evidence from a broad range of tasks and contexts that the activity of multiple muscles can appear to be well-approximated by only a few muscle synergies, each defined as a set of fixed relative levels of muscle activation ( d'Avella et al., 2003, 2006 Torres-Oviedo et al., 2006 Tresch and Jarc, 2009 Dominici et al., 2011 Roh et al., 2012). 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. 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. 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. Because EMG records are typically not available from all contributing muscles, we also explored reconstructions from incomplete sets of muscles. 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). 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 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. 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. 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. 2Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA.1Centre for Sensorimotor Neuroscience, School of Human Movement Studies, The University of Queensland, Brisbane, QLD, Australia. ![]()
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