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Data for: Hybrid dedicated and distributed coding in PMd/M1 provides separation and interaction of bilateral arm signals

Cite this dataset

Dixon, Tanner et al. (2021). Data for: Hybrid dedicated and distributed coding in PMd/M1 provides separation and interaction of bilateral arm signals [Dataset]. Dryad.


Pronounced activity is observed in both hemispheres of the motor cortex during preparation and execution of unimanual movements. The organizational principles of bi-hemispheric signals and the functions they serve throughout motor planning remain unclear. Using an instructed-delay reaching task in monkeys, we identified two components in population responses spanning PMd and M1. A “dedicated” component, which segregated activity at the level of individual units, emerged in PMd during preparation. It was most prominent following movement when M1 became strongly engaged, and principally involved the contralateral hemisphere. In contrast to recent reports, these dedicated signals solely accounted for divergence of arm-specific neural subspaces. The other “distributed” component mixed signals for each arm within units, and the subspace containing it did not discriminate between arms at any stage. The statistics of the population response suggest two functional aspects of the cortical network: one that spans both hemispheres for supporting preparatory and ongoing processes, and another that is predominantly housed in the contralateral hemisphere and specifies unilateral output.


Unit activity was collected using 24-32 channel multi-site probes (V-probe - Plexon Inc, Dallas, TX), with 15um diameter electrode contacts separated by 100um and positioned axially along a single shank. Probes were lowered deep enough to cover roughly the full laminar structure of cortex (Fig 2B-C). The depth of insertion was determined by (1) measurements of the dural surface prior to recording, and (2) presence of spiking activity across all channels. 2 probes were typically inserted in each hemisphere daily and removed at the end of the session, one in PMd and one in M1. A total of 12 insertion points across PMd and M1 of each hemisphere were used across 13 recording sessions in Monkey O, and 6 insertion points across 7 sessions for Monkey W (Fig 2A). 

Neural data were recorded using the OmniPlex Neural Recording Data Acquisition System (Plexon Inc, Dallas, TX). Spike sorting was performed offline (Offline Sorter – Plexon Inc, Dallas, TX). Single-unit waveforms were isolated in multi-dimensional feature space (including principal components, non-linear energy, waveform amplitudes) and rejected if either (1) the waveform clusters were not stable over the course of the session, (2) >0.4% of inter-spike-intervals were below 1ms, or (3) they were clearly repeats of a unit identified on an adjacent channel as determined visually by coincident spiking. To fully ensure that units were not double logged, we eliminated one member of any pair of units that had a firing rate correlation above 0.9 and were within two channels of each other. For population level analyses (PCA, LDA), a small number of multi-units were included. A multi-unit was defined by waveform clusters that separated from the noise cluster and were stable over time, but did not quite meet the inter-spike-interval criteria or contained what might be multiple unit clusters that could not be easily separated. For monkey O, the average proportion of multi-units in each single session population sample was 17%, ranging 12-25%. For monkey W, average 20%, ranging 12-32%.

Spiking data were binned in 20ms non-overlapping bins, square-root transformed to stabilize variance, and smoothed with a 50ms gaussian kernel for all analyses (Yu et al., 2009). This provided an effective sampling rate of 50Hz, with each sample time aligned to the center of its associated bin. The window edges were included in all analysis windows (e.g. 300ms windows included 16 samples).

Usage notes

Please feel free to contact Tanner Dixon by email at for help with using the dataset.


United States Department of Defense, Award: National Defense Science and Engineering Graduate Fellowship

National Institutes of Health, Award: NS097480