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task-relevant variability. Task-relevant variability does not affect selected performance variables.
M-mode is a set of muscles within which the CNS scales the activation level in parallel either in
time10 or space11 . Mathematically, a M-mode represents a unitary n-dimensional vector, where n
is the number of the muscles formed it and each n-dimension (vector’s components) represents the
fixed weighted contribution of the n-muscle to the M-mode. A muscle can participate in multiple
M-mode. The CNS controls the n-dimensional vector’s magnitude by scaling linearly its elements,
the weighting coefficients of the muscles (Latash, 2008b; Latash, 2012; Ting, 2007; Ting and
Chvatal, 2011). Following Ting (2007) and according the nomenclature of Figure (1.15), the net
activation pattern for any given muscle (m[N ×1] ) on the course of performing an action is a linear
combination of the sum of the fixed elements of the M-modes vectors W1→k that are structured
temporally by the scaling coefficients of the neural commands vectors C1→k
m[N ×1] =
Ci (t 1→N ) · w i
and the activation patterns of all muscles formed the M-modes at any given instant t is
m[1×n] =
ci (t) · Wi .
Therefore, the activation patterns of all muscles on the course of an action is
m[N ×n] =
Ci (t 1→N ) · Wi .
Several M-modes may form a muscle synergy, i.e., a neural organization that provides stability of
a performance variable by co-varied adjustments of its elements, the M-modes (Latash, 2008b;
Latash, 2012). Assuming that synergies are organized in a hierarchical control scheme, a M-mode
may be viewed as a performance variable itself, stabilized by a lower level synergy that uses
firing patterns of individual motor units as elements (Latash, 2008b; Latash, 2012). Assuming
that the M-modes are fixed throughout certain task repetitions, whereas their scaling factors are
varied (Ting and Chvatal, 2011), the low level synergy ensures that the proportion of the weighted
contribution of each muscle on the M-mode does not change. This is equivalent to the notion
that the low level synergy stabilizes the direction of the n-dimensional vector within the muscle
activation space, and mathematically represents the angles formed between the n-dimensional
vectors of the M-mode across tasks and repetitions (Fig. 1.15).
Recently, experiments showed that the organization of muscles into groups in complex whole-body
tasks can differ significantly across both task variations and subjects (Danna-Dos-Santos et al., 2009;
Frère et al., 2012), but either with similar temporal profiles of the gains at which the M-modes are
rectruited (Frère et al., 2012), or with gains that help stabilizing important mechanical variables
like COP shifts (Danna-Dos-Santos et al., 2009)—i.e., the ability to organize muscles co-variation
On the course of performing an action.
Across actions with different parameters.