christincha/multielectrode_grasp: This repository contains the datasets described in the publication Brochier et al. (2018). Massively parallel recordings in macaque motor cortex during an instructed delayed reach-to-grasp task, Scientific Data, 5, 180055. @ d8623d7d8120d75934b82b1240e8b47daeb1b01e

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Michael Denker eebd26f4ea Update frozen Neo to version 0.5.2		6 years ago
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AUTHORS	d1f7f85689 Initial commit of data and code of the Reach-to-Grasp Experiment.	7 years ago
CITATION.txt	d1f7f85689 Initial commit of data and code of the Reach-to-Grasp Experiment.	7 years ago
LICENSE.txt	d1f7f85689 Initial commit of data and code of the Reach-to-Grasp Experiment.	7 years ago
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README.rst	eebd26f4ea Update frozen Neo to version 0.5.2	6 years ago
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				README.rst
			
		
		
	
			
		===
Neo
===

Neo is a Python package for working with electrophysiology data in Python, together
with support for reading a wide range of neurophysiology file formats, including
Spike2, NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt, and support for
writing to a subset of these formats plus non-proprietary formats including HDF5.

The goal of Neo is to improve interoperability between Python tools for
analyzing, visualizing and generating electrophysiology data by providing a common,
shared object model. In order to be as lightweight a dependency as possible,
Neo is deliberately limited to represention of data, with no functions for data
analysis or visualization.

Neo is used by a number of other software tools, including OpenElectrophy_
and SpykeViewer_ (data analysis and visualization), Elephant_ (data analysis),
the G-node_ suite (databasing) and PyNN_ (simulations).

Neo implements a hierarchical data model well adapted to intracellular and
extracellular electrophysiology and EEG data with support for multi-electrodes
(for example tetrodes). Neo's data objects build on the quantities package,
which in turn builds on NumPy by adding support for physical dimensions. Thus
Neo objects behave just like normal NumPy arrays, but with additional metadata,
checks for dimensional consistency and automatic unit conversion.

A project with similar aims but for neuroimaging file formats is `NiBabel`_.

Code status
-----------

.. image:: https://travis-ci.org/NeuralEnsemble/python-neo.png?branch=master
   :target: https://travis-ci.org/NeuralEnsemble/python-neo
   :alt: Unit Test Status
.. image:: https://coveralls.io/repos/NeuralEnsemble/python-neo/badge.png
   :target: https://coveralls.io/r/NeuralEnsemble/python-neo
   :alt: Unit Test Coverage
.. image:: https://requires.io/github/NeuralEnsemble/python-neo/requirements.png?branch=master
   :target: https://requires.io/github/NeuralEnsemble/python-neo/requirements/?branch=master
   :alt: Requirements Status

More information
----------------

- Home page: http://neuralensemble.org/neo
- Mailing list: https://groups.google.com/forum/?fromgroups#!forum/neuralensemble
- Documentation: http://neo.readthedocs.io/
- Bug reports: https://github.com/NeuralEnsemble/python-neo/issues

For installation instructions, see doc/source/install.rst

:copyright: Copyright 2010-2016 by the Neo team, see doc/source/authors.rst.
:license: 3-Clause Revised BSD License, see LICENSE.txt for details.


.. _OpenElectrophy: https://github.com/OpenElectrophy/OpenElectrophy
.. _Elephant: http://neuralensemble.org/elephant
.. _G-node: http://www.g-node.org/
.. _Neuroshare: http://neuroshare.org/
.. _SpykeViewer: https://spyke-viewer.readthedocs.org/en/latest/
.. _NiBabel: http://nipy.sourceforge.net/nibabel/
.. _PyNN: http://neuralensemble.org/PyNN
.. _quantities: http://pypi.python.org/pypi/quantities
.. _`NeuralEnsemble mailing list`: http://groups.google.com/group/neuralensemble
.. _`issue tracker`: https://github.c

		
		

datacite.yml
Title	Massively parallel multi-electrode recordings of macaque motor cortex during an instructed delayed reach-to-grasp task
Authors	Brochier,Thomas;Institut de Neurosciences de la Timone (INT), UMR 7289, CNRS – Aix Marseille Université, Marseille, France;orcid.org/0000-0001-6948-1234 Zehl,Lyuba;Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany;orcid.org/0000-0002-5947-9939 Hao,Yaoyao;Institut de Neurosciences de la Timone (INT), UMR 7289, CNRS – Aix Marseille Université, Marseille, France;orcid.org/0000-0002-9390-4660 Duret,Margaux;Institut de Neurosciences de la Timone (INT), UMR 7289, CNRS – Aix Marseille Université, Marseille, France;orcid.org/0000-0002-6557-748X Sprenger,Julia;Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany;orcid.org/0000-0002-9986-7477 Denker,Michael;Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany;orcid.org/0000-0003-1255-7300 Grün,Sonja;Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany;orcid.org/0000-0003-2829-2220 Riehle,Alexa;Institut de Neurosciences de la Timone (INT), UMR 7289, CNRS – Aix Marseille Université, Marseille, France
Description	We provide two electrophysiological datasets recorded via a 10-by-10 multi-electrode array chronically implanted in the motor cortex of two macaque monkeys during an instructed delayed reach-to-grasp task. The datasets contain the continuous measure of extracellular potentials at each electrode sampled at 30 kHz, the local field potentials sampled at 1 kHz and the timing of the online and offline extracted spike times. It also includes the timing of several task and behavioral events recorded along the electrophysiological data. Finally, the datasets provide a complete set of metadata structured in a standardized format. These metadata allow easy access to detailed information about the datasets such as the settings of the recording hardware, the array specifications, the location of the implant in the motor cortex, information about the monkeys, or the offline spike sorting.
License	CC-BY (http://creativecommons.org/licenses/by/4.0/)
References	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, 5, 180055. [] (IsPartOf) Zehl, L., Jaillet, F., Stoewer, A., Grewe, J., Sobolev, A., Wachtler, T., … Grün, S. (2016). Handling Metadata in a Neurophysiology Laboratory. Frontiers in Neuroinformatics, 10, 26. [] (HasMetadata) Riehle, A., Wirtssohn, S., Grün, S., & Brochier, T. (2013). Mapping the spatio-temporal structure of motor cortical LFP and spiking activities during reach-to-grasp movements. Frontiers in Neural Circuits, 7, 48 [] (HasMetadata)
Funding	Helmholtz Association, Supercomputing and Modeling for the Human Brain EU, EU.604102 EU, EU.720270 DFG, DFG.GR 1753/4-2 DFG, DFG.DE 2175/2-1 RIKEN-CNRS, Collaborative Research Agreement ANR, GRASP CNRS, PEPS CNRS, Neuro_IC2010 DAAD LIA Vision for Action
Keywords	Neuroscience Electrophysiology Utah Array Spikes Local Field Potential Macaque Motor Cortex
Resource Type	Dataset