Michael Denker d1f7f85689 Initial commit of data and code of the Reach-to-Grasp Experiment. | 6 years ago | |
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KCSD.py | 6 years ago | |
README.md | 6 years ago | |
__init__.py | 6 years ago | |
basis_functions.py | 6 years ago | |
icsd.py | 6 years ago | |
test_data.mat | 6 years ago | |
utility_functions.py | 6 years ago |
Here, are CSD methods for different electrode configurations.
Keywords: Local field potentials; Current-source density; CSD; Multielectrode; Laminar electrode; Barrel cortex
1D - laminar probe like electrodes. 2D - Microelectrode Array like 3D - UtahArray or multiple laminar probes.
The following methods have been implemented here, for,
1D - StandardCSD, DeltaiCSD, SplineiCSD, StepiCSD, KCSD1D 2D - KCSD2D, MoIKCSD (Saline layer on top of slice) 3D - KCSD3D
Each of these methods listed have some advantages - except StandardCSD which is not recommended. The KCSD methods can handle broken or irregular electrode configurations electrode
Python-implementation of the inverse current source density (iCSD) methods from http://software.incf.org/software/csdplotter
The Python iCSD toolbox lives on GitHub as well: https://github.com/espenhgn/iCSD
The methods were originally developed by Klas H. Pettersen, as described in: Klas H. Pettersen, Anna Devor, Istvan Ulbert, Anders M. Dale, Gaute T. Einevoll, Current-source density estimation based on inversion of electrostatic forward solution: Effects of finite extent of neuronal activity and conductivity discontinuities, Journal of Neuroscience Methods, Volume 154, Issues 1Ð2, 30 June 2006, Pages 116-133, ISSN 0165-0270, http://dx.doi.org/10.1016/j.jneumeth.2005.12.005. (http://www.sciencedirect.com/science/article/pii/S0165027005004541)
To see an example of usage of the methods, see the file demo_icsd.py
This is 1.0 version of kCSD inverse method proposed in
J. Potworowski, W. Jakuczun, S. Łęski, D. K. Wójcik "Kernel Current Source Density Method" Neural Computation 24 (2012), 541–575
Some key advantages for KCSD methods are -- irregular grid of electrodes - accepts arbitrary electrode placement. -- crossvalidation to ensure no over fitting -- CSD is not limited to electrode positions - it can obtained at any location
For citation purposes, If you use this software in published research please cite the following work
[1] Potworowski, J., Jakuczun, W., Łęski, S. & Wójcik, D. (2012) 'Kernel current source density method.' Neural Comput 24(2), 541-575.
[2] Pettersen, K. H., Devor, A., Ulbert, I., Dale, A. M. & Einevoll, G. T. (2006) 'Current-source density estimation based on inversion of electrostatic forward solution: effects of finite extent of neuronal activity and conductivity discontinuities.' J Neurosci Methods 154(1-2), 116-133.
[3] Łęski, S., Pettersen, K. H., Tunstall, B., Einevoll, G. T., Gigg, J. & Wójcik, D. K. (2011) 'Inverse Current Source Density method in two dimensions: Inferring neural activation from multielectrode recordings.' Neuroinformatics 9(4), 401-425.
[4] Łęski, S., Wójcik, D. K., Tereszczuk, J., Świejkowski, D. A., Kublik, E. & Wróbel, A. (2007) 'Inverse current-source density method in 3D: reconstruction fidelity, boundary effects, and influence of distant sources.' Neuroinformatics 5(4), 207-222.
[5] Ness, T. V., Chintaluri, C., Potworowski, J., Łeski, S., Głabska, H., Wójcik, D. K. & Einevoll, G. T. (2015) 'Modelling and Analysis of Electrical Potentials Recorded in Microelectrode Arrays (MEAs).' Neuroinformatics 13(4), 403-426.
For your research interests of Kernel methods of CSD please see, https://github.com/Neuroinflab/kCSD-python
Contact: Prof. Daniel K. Wojcik
Here (https://github.com/Neuroinflab/kCSD-python/tree/master/tests), are scripts to compare different KCSD methods with different CSD sources. You can play around with the different parameters of the methods.
The implentation is based on the Matlab version at INCF (http://software.incf.org/software/kcsd), which is now out-dated. A python version based on this was developed by Grzegorz Parka (https://github.com/INCF/pykCSD), which is also not supported at this point. This current version of KCSD methods in elephant is a mirror of https://github.com/Neuroinflab/kCSD-python/commit/8e2ae26b00da7b96884f2192ec9ea612b195ec30
datacite.yml | |
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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 |
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 |