methods to calculate LFPs with spike events from network sim
Project description
Module hybridLFPy
Python module implementating a hybrid model scheme for predictions of extracellular potentials (local field potentials, LFPs) of spiking neuron network simulations.
Development
The module hybridLFPy was mainly developed in the Computational Neuroscience Group (http://compneuro.umb.no), Department of Mathemathical Sciences and Technology (http://www.nmbu.no/imt), at the Norwegian University of Life Sciences (http://www.nmbu.no), Aas, Norway, in collaboration with Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6), Juelich Research Centre and JARA, Juelich, Germany (http://www.fz-juelich.de/inm/inm-6/EN/).
Manuscript
A preprint of our manuscript on the hybrid scheme implemented in hybridLFPy is available on arXiv.org at http://arxiv.org/abs/1511.01681
Citation: Espen Hagen, David Dahmen, Maria L. Stavrinou, Henrik Linden, Tom Tetzlaff, Sacha Jennifer van Albada, Sonja Gruen, Markus Diesmann, Gaute T. Einevoll. Hybrid scheme for modeling local field potentials from point-neuron networks. arXiv:1511.01681 [q-bio.NC]
Bibtex source:
@ARTICLE{2015arXiv151101681H, author = {{Hagen}, E. and {Dahmen}, D. and {Stavrinou}, M.~L. and {Lind{\'e}n}, H. and {Tetzlaff}, T. and {van Albada}, S.~J. and {Gr{\"u}n}, S. and {Diesmann}, M. and {Einevoll}, G.~T.}, title = "{Hybrid scheme for modeling local field potentials from point-neuron networks}", journal = {ArXiv e-prints}, archivePrefix = "arXiv", eprint = {1511.01681}, primaryClass = "q-bio.NC", keywords = {Quantitative Biology - Neurons and Cognition}, year = 2015, month = nov, adsurl = {http://adsabs.harvard.edu/abs/2015arXiv151101681H}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} }
Tutorial slides
Slides from OCNS 2015 meeting tutorial T2: Modeling and analysis of extracellular potentials hosted in Prague, Czech Republic on LFPy and hybridLFPy: CNS2015_LFPy_tutorial.pdf
License
This software is released under the General Public License (see LICENSE file).
Warranty
This software comes without any form of warranty.
Installation
First download all the hybridLFPy source files using git (http://git-scm.com). Open a terminal window and type:
cd $HOME/where/to/put/hybridLFPy git clone https://github.com/INM-6/hybridLFPy.git
To use hybridLFPy from any working folder without installing files, add this path to $PYTHONPATH. Edit your .bash_profile or similar file, and add:
export $PYTHONPATH=$PYTHONPATH:/PATH/TO/THIS/FOLDER:
Installing it is also possible, but not recommended as things might change with any pull request from the repository:
(sudo) python setup.py install (--user)
examples folder
Some example script(s) on how to use this module
docs folder
Source files for autogenerated documentation using Sphinx.
To compile documentation source files in this directory using sphinx, use:
sphinx-build -b html docs documentation
Online documentation
The sphinx-generated html documentation can be accessed at http://INM-6.github.io/hybridLFPy
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