Are muscle synergies useful for neural control?

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-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.

Analysis of muscle synergies and activation–deactivation patterns in subjects with anterior cruciate ligament deficiency during walking

“…It is reported that there are similarities in the muscle synergies when performing the same movement across subjects. Several authors reported muscle synergies when walking (Clark et al, 2010;Dominici et al, 2011;Ivanenko et al, 2004;Neptune et al, 2009;Oliveira et al, 2014), walking with perturbations (Ivanenko et al, 2005) or performing other tasks (Rugy et al, 2013). Clark et al (2010) applied the muscle synergy analysis in post-stroke injured subjects.…”

Equifinality and its violations in a redundant system: multifinger accurate force production

“…One of these components (parallel to the UCM, V UCM ) does not affect the performance variable, whereas the other one (orthogonal to the UCM, V ORT ) does. There are other methods that study the structure of variance (reviewed in de Rugy et al 2013;Tresch and Jarc 2009; Tresch et al 2006), but the UCM-based analysis is particularly appropriate for our study because it provides direct links between variance in two spaces, those of a particular performance variable and of elemental variables.A scheme combining the principle of abundance, the hierarchical control scheme, the idea of synergies, the UCM hypothesis, and control with RC (Latash 2010) allows making predictions with respect to phenomena of equifinality at different levels of analysis of a redundant system. In particular, this scheme predicts that a transient perturbation may be expected to result in equifinality at the level of task-related performance variables defined by an invariant RC at the highAddress for reprint requests and other correspondence: M. L. Latash,…”

“…One of these components (parallel to the UCM, V UCM ) does not affect the performance variable, whereas the other one (orthogonal to the UCM, V ORT ) does. There are other methods that study the structure of variance (reviewed in de Rugy et al 2013;Tresch and Jarc 2009; Tresch et al 2006), but the UCM-based analysis is particularly appropriate for our study because it provides direct links between variance in two spaces, those of a particular performance variable and of elemental variables.…”

Generating Human-Like Velocity-Adapted Jumping Gait from sEMG Signals for Bionic Leg’s Control

“…Once the decoder was trained, it is used in real time to estimate the multijoint angles and map them to output of myoelectric interface. Moreover, linear combinations of synergies are capable of describing complex force and motion patterns in reduced dimensions [33]. Therefore the robust representations of synergies within the control scheme can generate flexible gaits for other complex motions, such as hopping and running with user's intent.…”