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Brochier T, Zehl L, Hao Y, et al. Massively parallel recordings in macaque motor cortex during an instructed delayed reach-to-grasp task. Sci Data. 2018;5:180055doi: 10.1038/sdata.2018.55.
Brochier, T., Zehl, L., Hao, Y., Duret, M., Sprenger, J., Denker, M., Grün, S., & Riehle, A. (2018). Massively parallel recordings in macaque motor cortex during an instructed delayed reach-to-grasp task. Scientific data, 5180055. https://doi.org/10.1038/sdata.2018.55
Brochier, Thomas, et al. "Massively parallel recordings in macaque motor cortex during an instructed delayed reach-to-grasp task." Scientific data vol. 5 (2018): 180055. doi: https://doi.org/10.1038/sdata.2018.55
Brochier T, Zehl L, Hao Y, Duret M, Sprenger J, Denker M, Grün S, Riehle A. Massively parallel recordings in macaque motor cortex during an instructed delayed reach-to-grasp task. Sci Data. 2018 Apr 10;5:180055. doi: 10.1038/sdata.2018.55. PMID: 29633986; PMCID: PMC5892370.
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Sci Data. 2018 Apr 10;5:180055. doi: 10.1038/sdata.2018.55.

Massively parallel recordings in macaque motor cortex during an instructed delayed reach-to-grasp task.

Scientific data

Thomas Brochier, Lyuba Zehl, Yaoyao Hao, Margaux Duret, Julia Sprenger, Michael Denker, Sonja Grün, Alexa Riehle

Affiliations

  1. Institut de Neurosciences de la Timone (INT), UMR 7289, CNRS-Aix Marseille Université, 13005 Marseille, France.
  2. Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (INM-10), 52425 Jülich, Germany.
  3. Theoretical Systems Neurobiology, RWTH Aachen University, 52056 Aachen, Germany.
  4. RIKEN Brain Science Institute, Wako-Shi 351-0198 Saitama, Japan.

PMID: 29633986 PMCID: PMC5892370 DOI: 10.1038/sdata.2018.55

Abstract

We publish two electrophysiological datasets recorded in motor cortex of two macaque monkeys during an instructed delayed reach-to-grasp task, using chronically implanted 10-by-10 Utah electrode arrays. We provide a) raw neural signals (sampled at 30 kHz), b) time stamps and spike waveforms of offline sorted single and multi units (93/49 and 156/19 SUA/MUA for the two monkeys, respectively), c) trial events and the monkey's behavior, and d) extensive metadata hierarchically structured via the odML metadata framework (including quality assessment post-processing steps, such as trial rejections). The dataset of one monkey contains a simultaneously saved record of the local field potential (LFP) sampled at 1 kHz. To load the datasets in Python, we provide code based on the Neo data framework that produces a data structure which is annotated with relevant metadata. We complement this loading routine with an example code demonstrating how to access the data objects (e.g., raw signals) contained in such structures. For Matlab users, we provide the annotated data structures as mat files.

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