Two-target cycling task, primate motor cortex

This dataset contains single-unit electrophysiology data from two rhesus macaque monkeys during the performance of a two-target cycling task first described in Russo et al Neuron 2018. For more information about data collection and preprocessing see Russo et al Neuron 2018 and Russo et al Neuron 2020. For use examples and more example kinematics from the task, see the following GitHub repositories: • https://github.com/aarusso/trajectory-tangling • https://github.com/aarusso/trajectory-divergence Each dataset (one for each monkey) contains muscle activity (EMG), neural activity recorded from primary motor cortex and dorsal premotor cortex (M1), and neural activity recorded from the supplementary motor area (SMA). The dataset is organized as a MATLAB struct with the following fields: • .xA: mean-centered and normalized, trial-averaged responses for primary motor cortical neurons in a TC x N matrix, where N corresponds to the number of neurons, and TC corresponds to timepoints concatenated across all conditions. • .xA_raw: as for .xA but before normalization and mean-centering. • .xA_sem: corresponds to the standard error of the mean for the data in .xA_raw before trial-averaging. • xNames: unique name identifier for each neuron Analogous fields pertain to the SMA (‘.uA’) and EMG (‘.zA’) data. All data were recorded at 1kHz. The fields .vA and .pA contain example hand velocity (vertical and horizontal hand velocity) and example hand position (vertical and horizontal hand position) for 4 example sessions. The datasets also contain a field .mask which is a structure containing information about the TC dimension in all aforementioned fields: • .condNum: a TC x 1 vector containing the numeric identifier (1-20, for each of the 20 conditions) corresponding to that element of the TC dimension across all fields. • .time: a TC x 1 vector corresponding to time in seconds within each condition with respect to movement onset. • .dist: a TC x 1 vector corresponding to the pedaling distance. May be 7 (7-cycles), 4 (4-cycles), 2, 1, 0.5. • .dir: a TC x 1 vector corresponding to the pedaling direction. May be 1 (forward pedaling) or -1 (backward pedaling). • .pos: a TC x 1 vector corresponding to the starting position. May be 0 (bottom-start) or 0.5 (top-start). To ensure proper use of the data, please review the method sections of Russo et al 2018 and review demos provided in the GitHub repositories linked at the top of this document.本数据集包含两只恒河猴在完成Russo等人2018年发表于《神经元》(Neuron)的双目标骑行任务时采集的单单位电生理数据。关于数据采集与预处理的详细信息，请参阅Russo等人2018年和2020年发表于《神经元》的相关论文。如需该任务的使用示例与更多运动学数据，请参考以下GitHub仓库： • https://github.com/aarusso/trajectory-tangling • https://github.com/aarusso/trajectory-divergence 每只恒河猴对应一份数据集，其中包含肌肉活动（肌电图，EMG）、从初级运动皮层与背侧前运动皮层（M1）记录的神经活动，以及从辅助运动区（SMA）记录的神经活动。该数据集以MATLAB结构体形式组织，包含以下字段： • .xA：经均值中心化与归一化处理的初级运动皮层神经元试次平均响应，存储为TC×N矩阵，其中N为神经元数量，TC为所有实验条件下拼接的时间点总数。 • .xA_raw：与.xA格式一致，但未经过归一化与均值中心化处理。 • .xA_sem：对应.xA_raw在试次平均前的均值标准误。 • xNames：每个神经元的唯一名称标识符。 类似的字段分别适用于辅助运动区（SMA，以“.uA”为前缀）与肌电图（EMG，以“.zA”为前缀）数据。所有数据均以1kHz的采样率采集。字段.vA与.pA分别包含4个示例实验会话的手部速度（垂直与水平手部速度）与手部位置（垂直与水平手部位置）示例数据。 数据集还包含一个名为.mask的结构体，用于存储前述所有字段中TC维度的相关信息： • .condNum：TC×1向量，包含对应所有字段中TC维度元素的数字标识符（1至20，对应20种实验条件）。 • .time：TC×1向量，表示每种实验条件下相对于运动起始的时间（单位：秒）。 • .dist：TC×1向量，表示骑行距离，可选值为7（7圈）、4（4圈）、2、1、0.5。 • .dir：TC×1向量，表示骑行方向，可选值为1（正向骑行）或-1（反向骑行）。 • .pos：TC×1向量，表示起始位置，可选值为0（底部起始）或0.5（顶部起始）。 为确保数据的正确使用，请仔细研读Russo等人2018年的方法学部分，并参考本文档顶部链接的GitHub仓库中提供的演示代码。