DOI: 10.5061/dryad.zgmsbccnt
title: Data from: Consistent coordination patterns provide near perfect behavior decoding in a comprehensive motor program for insect flight

description: Precise spike timing encoding is present in many motor systems, but is not
 frequently utilized to decode behavior or to examine how coordination is
 achieved across many motor units. Furthermore, testing whether the same
 coordinated sets of muscles control different movements is difficult
 without a complete motor representation at the level of the currency of
 control – action potentials. Here, we demonstrate nearly perfect decoding
 of six hawk moth flight behavior states elicited in response to wide-field
 drifting visual stimuli about the flight axes – pitch, roll, and yaw –
 using a comprehensive, spike-resolved motor program. A simple linear
 decoding pipeline is sufficient to predict behavior, but only if precise
 timing information in included. We also demonstrate that as few as half
 the muscles can be used to retain this near perfect decoding performance,
 linking coordination to redundancy in encoding across the entire moth
 flight motor program. We then use this comprehensive motor representation
 to test if muscle covariation present in one pair of visual stimulus
 conditions can be used to decode behavior in a different pair of visual
 stimulus conditions. We find conserved muscle coordination patterns at the
 level of motor unit spike timings in these functionally distinct
 behaviors.
description: EMGs were taken from the 10 most important muscles for wing
 control from Manduca sexta while tethered moths flapped in a visual arena.
 The responded to a visual stimulus while simultaneous recordings of the
 spike resolved motor program and 6 DoF forces and torques were acquired.
 The comprehensive motor program of the moth, Manduca sexta, is composed of
 the 10 muscles (5 bilateral pairs) that accomplish nearly all control of
 the wings. On each side of the animal there is a primary flight power
 muscle for the downstroke (Dorsolongitudinal muscle –DLM) and one for the
 upstroke (Dorsoventral muscle –DVM). There are also three small muscles
 that produce an order of magnitude less force, but tweak, tension, and
 pitch the wingstroke to achieve control (“steering” muscles –basalar,
 subalar, 3rdaxillary). Each of these muscles is composed of one or few
 motor unit(s) and so we can read off the individual spikes from each
 recording. This gives us a precisely timed, spike resolved motor program
 that is nearly complete –any sensory information thataffects wing
 movements should manifest as changes in the number and timing of these
 muscle spikes In each case the animal is flapping its wings at roughly
 20-22 Hz but there is some variation. Using a Hilbert transform of the
 z-force recordings, we have cut the data up into comparable wingstrokes
 which can be used as a unit of analysis. Data has been collected from ~10
 moths, with multiple flight bouts in each case. A flight bout typically
 lasts about 20 secs. We have checked that the animal is flapping ata
 reasonable frequency and trimmed the bouts if the animal stops or starts
 flapping before the recording starts or stops. Wingstrokes from all bouts
 are currently considered equivalently. There are significant differences
 between individual animals, but each animal had typically 300-1000
 wingstrokes recorded. In all cases the neuromuscular recordings have been
 spike-sorted to identify the spikes. We do not recommend using the
 continuous voltage traces from the electrophysiological recordings because
 they can contain movement artifacts which could contain motor information.
 The identified spikes have been organized into spike trains (for
 contiguous data) and spike times (for wingstroke cut data).
 “Hard Turns” This dataset was collected with projected wide-field
 motion stimuli in each of the three rotational axes (yaw, pitch, and
 roll). We rotated the wide-field motion stimulus at a constant rotational
 velocity in one of the six directions: 3 axes, two signs in each case to
 produce hard left, hard right, hard pitch up, hard pitch down, hard roll
 left, hard roll right turns). Each individual has 100s of wingstrokes
 sampled for each condition which we can consider as repeated observations
 of the same behavioral condition. There is some variation from flight bout
 to flight bout that could be considered and there is
 individual-to-individual variation as in other datasets. This data has
 been cut up into individual wingstrokes and includes forces and torques
 sampled for 50 ms for each wingstroke (again 500 samples). This dataset
 includes 100s of wingstrokes form each of the six conditions with complete
 (10 muscle) recordings from 4 animals (complete data) and comparable
 amounts of data from 5 other moths that have only 9 muscle recordings in
 each case (incomplete data).
description: # Data for : Consistent coordination patterns provide near perfect
 behavior decoding in a comprehensive motor program for insect flight
 [https://doi.org/10.5061/dryad.zgmsbccnt](https://doi.org/10.5061/dryad.zgmsbccnt) ## Description of the data and file structure **Files and variables** File: Moth01_20200113_AnnotatedToShare_v3.mat, Moth02_20200114_AnnotatedToShare_v3.mat, Moth03_20200114_AnnotatedToShare_v3.mat, Moth04_20200114_AnnotatedToShare_v3.mat, Moth05_20200114_AnnotatedToShare_v3.mat, Moth06_20200114_AnnotatedToShare_v3.mat, Moth07_20200114_AnnotatedToShare_v3.mat, Moth08_20200114_AnnotatedToShare_v3.mat, Moth09_20200114_AnnotatedToShare_v.mat Description: Combined data for each moth (01 to 09) This dataset is from N = 9 moths that responded to six different stimulus conditions, for approx. 20 seconds per condition. We simultaneously recorded the six forces and torques produced (FT) and the spiking activity from 10 muscles, 5 on the left (L) and right (R) sides each: the axillary (AX), basalar (BA), subalar (SA), dorsoventral (DVM), and dorsolongitudinal (DLM) muscles. Each file corresponds to an individual moth and is named by Moth#_dateOfExperiment_Annotated_for_Sharing.mat Within each .mat file for each moth, all data is partitioned into 1 x 6 cell structures, with the cell structures corresponding to a visual stimulus. The columns of the cell structures contain data for the visual stimuli: 1 - pitch up @ 100 deg/s 2 - pitch down @ 100 deg/s 3 - roll left @ 100 deg/s 4 - roll right @100 deg/s 5 - yaw left @ 100 deg/s 6 - yaw right @ 100 deg/s All variables in the .mat file have this 1x6 structure and follow the same order. Each variable is described in more detail below: continuous_FT_data (1 x 6 cell): This is the continuous (non-wing stroke segmented) force-torque data. Within each cell is a S x 8 matrix, with S being the # of samples collected at 10000 Hz. The columns of this matrix report: 1 - time (some times are negative due to using a post-triggering collection scheme. These times are aligned to the spike timings in the muscle_idx structures) 2 - Fx (N) 3 - Fy (N) 4 - Fz (N) 5 - Tx (mN-m) 6 - Ty (mN-m) 7 - Tz (mN-m) 8 - Trigger Signal (this was used to align the spiking data to the FT data and can be ignored) These data have been transformed to the estimated center of mass for the moths and are in the body-attached frame (axes point in earth coordinates with center at moth COM). Fx_by_WS, Fy_by_WS, Fz_by_WS, Tx_by_WS, Ty_by_WS, Tz_by_WS: Here, the continuous FT data has been segmented into wing strokes, using the technique described in Putney et al. 2019. In brief, the Fz force was transformed to a periodic signal and the phase of the peak downward force was chosen as the start of the wing stroke. The data in all these structures is of form w x L where w is the # of wing strokes and L is the length of the longest wing stroke. NaN values in any columns correspond to empty variables. Accordingly, the wing strokes vary in length. Each column corresponds to a sample, again sampled at 10000 Hz. Forces are reported in N and torques in mN-m, as previously. ws_idx: Each cell contains a vector of the times of the start of each wing stroke from the segmentation process described above in units of seconds. ws_ave_FT_data: Each cell contains a w x 6 matrix, with w as the number of wing strokes, and the columns reporting the average force or torque produced during that wing stroke. The columns of the matrix report: 1 - Fx (N) 2 - Fy (N) 3 - Fz (N) 4 - Tx (mN-m) 5 - Ty (mN-m) 6 - Tz (mN-m) Possible muscle names: LAX, LBA, LSA, LDVM, LDLM, RAX, RBA, RSA, RDVM, RDLM Any data in muscleName_idx, muscleName_spikes, and muscleName_times reported as 'n/a' means that we were unable to collect data for that muscle and that visual stimulus condition for that moth. Empty matrices represent that the recording was good, but no spikes were recorded. muscleName_idx: Each cell contains a vector with all the continuous spike times for each visual stimulus condition. These times have not been segmented into wing strokes and represented the spike train across the entire trial in units of seconds. muscleName_spikes: Each cell contains a vector of w x 1 with w as the number of wing strokes, segmented as previously and the # of spikes that muscle fired in each wing stroke is reported. muscleName_times: Each cell contains a matrix w x maxSpikes with w as the number of wing strokes, and the times of each spike within that wing stroke reported in ms from the start of the wing stroke. Any NaN values are there merely to fill the wing stroke, as the # of spikes in each wing stroke is variable. Moth IDs that have all 10 muscles: 2, 5, 7, and 8.