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methods to calculate extracellular signals of neural activity from spike events from spiking neuron networks

Project description

hybridLFPy

Python module implementing a hybrid scheme for predictions of extracellular potentials (local field potentials, LFPs) of spiking neuron network simulations.

Project Status

DOI Documentation Status Upload Python Package Python pytest License

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/).

Citation

Should you find hybridLFPy useful for your research, please cite the following paper:

Espen Hagen, David Dahmen, Maria L. Stavrinou, Henrik Lindén, Tom Tetzlaff,
Sacha J. van Albada, Sonja Grün, Markus Diesmann, Gaute T. Einevoll;
Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks,
Cerebral Cortex, Volume 26, Issue 12, 1 December 2016, Pages 4461–4496,
https://doi.org/10.1093/cercor/bhw237

Bibtex source:

@article{doi:10.1093/cercor/bhw237,
author = {Hagen, Espen and Dahmen, David and Stavrinou, Maria L. and Lindén,
  Henrik and Tetzlaff, Tom and van Albada, Sacha J. and Grün, Sonja and
  Diesmann, Markus and Einevoll, Gaute T.},
title = {Hybrid Scheme for Modeling Local Field Potentials from
  Point-Neuron Networks},
journal = {Cerebral Cortex},
volume = {26},
number = {12},
pages = {4461-4496},
year = {2016},
doi = {10.1093/cercor/bhw237},
URL = { + http://dx.doi.org/10.1093/cercor/bhw237},
eprint = {/oup/backfile/content_public/journal/cercor/26/12/10.1093_cercor_bhw237/2/bhw237.pdf}
}

License

This software is released under the General Public License (see the 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 copying files, run:

(sudo) pip install -e . (--user)

Installing it is also possible, but not recommended as things might change with future pulls from the repository:

(sudo) pip install . (--user)

examples folder

Some example script(s) on how to use this module

docs folder

Source files for autogenerated documentation using Sphinx (https://www.sphinx-doc.org).

To compile documentation source files in this directory using sphinx, use:

sphinx-build -b html docs documentation

Dockerfile

The provided Dockerfile provides a Docker container recipe for x86_64 hosts with all dependencies required to run simulation files provided in examples. To build and run the container locally, get Docker from https://www.docker.com and issue the following (replace <image-name> with a name of your choosing):

docker build -t <image-name> -< Dockerfile
docker run -it -p 5000:5000 <image-name>:latest

The --mount option can be used to mount a folder on the host to a target folder as:

docker run --mount type=bind,source="$(pwd)",target=/opt/hybridLFPy \
-it -p 5000:5000 <image-name>

Then, code examples may be run as:

cd /opt/hybridLFPy/examples
nrnivmodl  # compile local .mod (NMODL) files
mpirun --allow-run-as-root python3 example_brunel.py

Online documentation

The sphinx-generated html documentation can be accessed at https://hybridLFPy.readthedocs.io

Project details


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