hybridlfpy
Biophysics-based prediction of LFPs from point-neuron networks
Science Score: 33.0%
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 7 DOI reference(s) in README -
✓Academic publication links
Links to: zenodo.org -
✓Committers with academic emails
2 of 6 committers (33.3%) from academic institutions -
○Institutional organization owner
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○Scientific vocabulary similarity
Low similarity (15.1%) to scientific vocabulary
Repository
Biophysics-based prediction of LFPs from point-neuron networks
Basic Info
Statistics
- Stars: 10
- Watchers: 20
- Forks: 10
- Open Issues: 8
- Releases: 8
Metadata Files
README.md
hybridLFPy
Python module implementing a hybrid scheme for predictions of extracellular potentials (local field potentials, LFPs) of spiking neuron network simulations.
Project Status
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
Owner
- Name: INM-6 & IAS-6
- Login: INM-6
- Kind: organization
- Location: Jülich, Germany
- Website: http://www.fz-juelich.de/inm/inm-6
- Repositories: 49
- Profile: https://github.com/INM-6
Computational and Systems Neuroscience & Theoretical Neuroscience
GitHub Events
Total
Last Year
Committers
Last synced: over 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Espen Hagen | e****n@f****e | 116 |
| Espen Hagen | 2****n | 40 |
| mstavrin | m****n@u****o | 20 |
| Espen Hagen | e****n@n****o | 14 |
| Espen Hagen | e****e@f****o | 2 |
| espenhgn | e****n@g****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 11 months ago
All Time
- Total issues: 41
- Total pull requests: 44
- Average time to close issues: 4 months
- Average time to close pull requests: 19 days
- Total issue authors: 4
- Total pull request authors: 2
- Average comments per issue: 1.17
- Average comments per pull request: 0.18
- Merged pull requests: 39
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- espenhgn (37)
- HilbertHuangHitomi (2)
- torbjone (1)
- mbesserve (1)
Pull Request Authors
- espenhgn (39)
- mstavrin (5)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 11 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 7
- Total maintainers: 1
pypi.org: hybridlfpy
methods to calculate extracellular signals of neural activity from spike events from spiking neuron networks
- Homepage: https://github.com/INM-6/hybridLFPy
- Documentation: https://hybridlfpy.readthedocs.io/
- License: LICENSE
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Latest release: 0.1.3
published about 10 years ago
Rankings
Maintainers (1)
Dependencies
- c-compiler
- cxx-compiler
- cython
- ffmpeg
- h5py
- ipython
- jupyter
- matplotlib
- meautility
- neuron
- numpy
- openmpi
- pip
- pytest
- python >=3.7
- scipy
- LFPy >=2.2
- LFPy >=2.2
- matplotlib *
- mpi4py *
- numpy *
- pytest *
- scipy *
- setuptools >=23.1.0
- Cython >=0.20
- LFPy >=2.2
- h5py >=2.5
- mpi4py >=1.2
- numpy >=1.8
- scipy >=0.14