To test if the rejection of the synergy hypothesis was due to statistical bias introduced by not including a linear combination of tR and tL in the original UmatriX of motor signals, we repeated the entire analysis including At in U.

We ask whether this pair of muscles acts as a muscle synergy (a single linear combination of activity) consistent with their hypothesized function of producing a left-right power differential.

We pose two synergy models, constructed as different linear combinations of the motor signals.

In their most general sense, muscle synergies are linear combinations of variables describing muscle activation that capture variation with fewer dimensions than the complete set of variables [9].

We found that retaining the timing of both DLM activations captured more of the variance in torque than compressing these timing variables into a single linear combination , or synergy (Fig 4D and 4E).

For the present analysis, we constructed synergies from linear combinations of the motor signals, U, as a separate step from the PLS feature identification to be consistent with prior synergy analyses [9,32].

We have proposed a flexible yet tractable form for the nonlinearity in terms of linear combinations of basis functions cascaded with a second output nonlinearity.

., 11¢, which are linearly combined with weights a,- and then passed through a scalar nonlinearity 9.

fm(xm)), where each function is parametrized with a linear combination of “bump” functions.

Thus the gene expression data for each perturbation p is mapped into the space spanned by linear combinations of the first k gene-expression SVD eigenvectors 1/1,.

The solution lies in a third key observation: the gene-expression profiles (vectors) of heterogeneous populations of cells are weighted linear combinations of the expression profiles (vectors) of the component states within the population, with the weights in this linear combination corresponding to cell-state proportions.

Several computational algorithms have been designed precisely for this purpose—to infer the constituent components of mixed signals—under the assumption that the mixed signal is a weighted linear combination of constituent components.

3A) and (ii) six more optimal pathways are a linear combination of vertices (or of the two convex combinations) and the lineality (with flux through {R3, R4} rather than {R2}).

In this case we can construct non-decomposable optimal flux pathways that are not vertices by taking, for instance, a linear combination of a vertex with a connected lineality.

In words, vertices can be summed by a conveX combination, rays can be summed as a conical combination, and linealities can be summed as a linear combination .