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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

Release History

Release History

This version
History Node

0.1.3

History Node

0.1.2

History Node

0.1.1

Download Files

Download Files

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
hybridLFPy-0.1.3.tar.gz (143.9 kB) Copy SHA256 Checksum SHA256 Source Jun 29, 2016

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