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Selective modulation of cortical population dynamics during neuroprosthetic skill learning

posted on 2022-09-14, 19:37 authored by Ellen ZippiEllen Zippi, Albert You, Karunesh Ganguly, Jose M. Carmena

Datasets analyzed in Zippi, You, et al., 2022 to study cortical population dynamics during neuroprosthetic skill learning in populations of neurons whose activity serves as direct input to a brain-machine interface decoder (direct subpopulation) and populations whose activity was not directly input to the BMI decoder (indirect subpopulation).

Data were originally recorded and published by Ganguly & Carmena, 2009. Neural recordings were made using the MAP system (Plexon, Dallas TX). Stable units, to be part of the direct ensemble, were selected based on waveform shape, amplitude, relationship to other units on the same channel, interspike interval distribution, and the presence of an absolute refractory period. Only units from primary motor cortex were used which had a clearly identified waveform with signal-to-noise ratio of at least 4:1. Activity was sorted prior to recording sessions using an online spike-sorting application (Sort Client; Plexon). Stability of waveforms was confirmed by analyzing the stability of PCA projections over days (Wavetracker; Plexon).

Data consists of a MATLAB structure for each subject. Data are divided into 15 epochs consisting of 150 self-initiated trials. Each epoch contains binned spike counts for each trial (units, bins) as well as the corresponding x and y cursor position in each bin. The binned spike counts are separated by direct and indirect neurons. Indirect neurons are further separated into near (those recorded on the same electrode as a direct neuron) and far (those recorded on a separate electrode). "Rearranged neural data" represents the neural data after stability in the indirect neural recordings was assessed using the methods described in Fraser and Schwartz, 2012. In these matrices, the index in the first dimension represents the ID of the neuron across epochs. A second MATLAB structure for each subject with data for each epoch separated by target. An additional MATLAB structure containing behavioral data is included per subject. These structures contain an array of the target IDs, trial length, and whether or not the self-initiated trial was successful of length all trials. 


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