Data from: Dual dimensionality reduction reveals independent encoding of motor features in a muscle synergy for insect flight control

What are the features of movement encoded by changing motor commands? Do
motor commands encode movement independently or can they be represented in
a reduced set of signals (i.e. synergies)? Motor encoding poses a
computational and practical challenge because many muscles typically drive
movement, and simultaneous electrophysiology recordings of all motor
commands are typically not available. Moreover, during a single locomotor
period (a stride or wingstroke) the variation in movement may have high
dimensionality, even if only a few discrete signals activate the muscles.
Here, we apply the method of partial least squares (PLS) to extract the
encoded features of movement based on the cross-covariance of motor
signals and movement. PLS simultaneously decomposes both datasets and
identifies only the variation in movement that relates to the specific
muscles of interest. We use this approach to explore how the main
downstroke flight muscles of an insect, the hawkmoth Manduca sexta, encode
torque during yaw turns. We simultaneously record muscle activity and
turning torque in tethered flying moths experiencing wide-field visual
stimuli. 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. Alternatively, each
muscle might individually encode variation in movement. We show that PLS
feature analysis produces an efficient reduction of dimensionality in
torque variation within a wingstroke. At first, the two muscles appear to
behave as a synergy when we consider only their wingstroke-averaged
torque. However, when we consider the PLS features, the muscles reveal
independent encoding of torque. Using these features we can predictably
reconstruct the variation in torque corresponding to changes in muscle
activation. PLS-based feature analysis provides a general two-sided
dimensionality reduction that reveals encoding in high dimensional sensory
or motor transformations.