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LFPsimpy-0.1.1
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مشاهده بیشترتوضیحات
Zero-model-modification, MPI-compatible Python package for computing NEURON simulator model local field potentials (LFPs)
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | LFPsimpy-0.1.1 |
| نام | LFPsimpy |
| نسخه کتابخانه | 0.1.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Justas Birgiolas |
| ایمیل نویسنده | justas@asu.edu |
| آدرس صفحه اصلی | https://github.com/justasb/LFPsimpy |
| آدرس اینترنتی | https://pypi.org/project/LFPsimpy/ |
| مجوز | - |
[](https://travis-ci.com/JustasB/LFPsimpy)
[](https://codecov.io/gh/JustasB/LFPsimpy)
[](https://badge.fury.io/py/LFPsimpy)
# LFPsimpy: A zero-model-modification, MPI-compatible Python package to compute Local Field Potentials of [NEURON simulator](https://neuron.yale.edu) models
**Zero-modification:** With LFPsimpy, there is no need to modify or re-write a NEURON model to fit a particular pattern or style. Given an existing NEURON model (HOC/Python/cell/network), just add a few Python lines to specify the location and parameters of the LFP electrode. Then run the simulation and plot or further process the LFP signal.
**Python-based:** The package is written in pure Python. Download the Python source code and modify or extend it using a familiar language. A small [.HOC file](https://www.neuron.yale.edu/neuron/static/new_doc/programming/hocsyntax.html) allows plotting the LFP signal using native NEURON graphs.
**Multiple LFP algorithms:** `Line`, `Point`, and `RC` methods of [Parasuram et. al. (2016)]( http://journal.frontiersin.org/article/10.3389/fncom.2016.00065/abstract) are implemented. Extend the Python source code to use a custom algorithm.
**Unlimited electrodes:** Place any number of LFP electrodes in arbitrary 3D locations to simulate multi-electrode arrays.
**MPI-compatible:** The package works with single- and multi-process simulations. Rank 0 contains the electrode values of the whole model.
# Requirements
LFPsimpy requires a working version of NEURON 7.5+ either installed from a [package/installer](https://www.neuron.yale.edu/neuron/download) (easier) or [compiled](https://neurojustas.com/2018/03/27/tutorial-installing-neuron-simulator-with-python-on-ubuntu-linux/) (more challenging). Linux, Mac, and Windows versions are supported.
You must be able to run at least *one* of these commands in a terminal window without errors:
- `nrniv -python`
- Or `python -c 'from neuron import h'`
If you cannot run any of these commands, it indicates that there is something amiss with your NEURON installation. Search the error messages on the [NEURON forum](https://www.neuron.yale.edu/phpBB/) for help.
# Installation
Installation depends on how you installed NEURON simulator (installed vs. compiled).
## If you installed a downloaded NEURON package
Download and extract [this LFPsimpy ZIP file](https://github.com/JustasB/LFPsimpy/archive/master.zip) to a known folder. Then note the location of the `LFPsimpy` sub-folder.
Then append the `LFPsimpy` parent folder location to your `$PYTHONPATH` environmental variable. E.g. `export PYTHONPATH=$PYTHONPATH:/path/to/LFPsimpy-master/`. Place the line in your shell startup file (e.g. `~/.bashrc`) to ensure the variable remains set after an OS restart.
## If you compiled NEURON+Python
To install the library, simply type in `pip install lfpsimpy` in your terminal.
# Usage
To use the library, first load your HOC or Python model in NEURON, insert LFP electrode(s), run simulation, and plot/process the electrode signal.
```
# Load your cell or network model
from neuron import h
run_scripts_build_model_etc()
# Load the LFP library
from LFPsimpy import LfpElectrode
# Place an LFP electrode
# x,y,z in microns
# sampling_period in ms. E.g. 0.1 => 10kHz
# method: either 'Line', 'Point', or 'RC'. See: Parasuram et. al. (2016)
le = LfpElectrode(x=100, y=50, z=0, sampling_period=0.1, method='Line')
# Run the simulation
h.tstop = 100 # <- important!
h.run()
# Plot/process LFP values
le.times # Contains the sampled LFP times
le.values # Contains the corresponding sampled LFP voltage values (nV)
```
**More examples** are described in [this Jupyter notebook](https://github.com/JustasB/hoc2swc/blob/master/examples.ipynb).
# NEURON GUI plotting
When using the NEURON GUI, after the electrode is inserted, you can plot the LFP electrode value with:
`Graph > Current Axis > Plot What? > Objects > LfpElectrode[0].value`
then
`In Tools > RunControl, set Points plotted/ms to 1/sampling_period`
`Init & Run` will show the LFP value of the first inserted electrode
# How It Works
LFPsimpy is a Python re-implementation of [LFPsim](https://github.com/compneuro/LFPsim) described in [Parasuram et. al. (2016)]( http://journal.frontiersin.org/article/10.3389/fncom.2016.00065/abstract). The original publication estimated LFPs using three different methods and also did not require a NEURON model to be in a specific format. However, the original implementation is in HOC, is not MPI-compatible, and places restrictions on the number of electrodes that can be placed in a simulation.
This library encapsulates the three LFP estimation methods described in the paper and uses the more efficient NEURON's [`i_membrane_`](https://www.neuron.yale.edu/neuron/static/new_doc/simctrl/cvode.html#CVode.use_fast_imem) method. These changes allow arbitrary number of electrodes and allows computing the LFP in MPI-parallelized models.
# Issues
While NEURON allows running simulations past the `tstop` value, this library does not support this usage pattern. If `h.t` exceeds `h.tstop` a warning is shown and the LFP signal is not computed.
If you encounter an issue, first make sure it's not due to NEURON itself. If it is, please contact the [NEURON team](https://www.neuron.yale.edu/phpBB/). If the issue is with this library, please create an [issue on Github](https://github.com/JustasB/LFPsimpy/issues).
# Contributing
To contribute, please open an issue first and discuss your plan for contributing. Then fork this repository and commit a pull-request with your changes.
# Acknowledgements
LFPsimpy is a Python re-implementation of [LFPsim](https://github.com/compneuro/LFPsim) described in [Parasuram et. al. (2016)]( http://journal.frontiersin.org/article/10.3389/fncom.2016.00065/abstract). When using LFPsimpy in research projects, please cite the original publication and this repository.
نحوه نصب
نصب پکیج whl LFPsimpy-0.1.1:
pip install LFPsimpy-0.1.1.whl
نصب پکیج tar.gz LFPsimpy-0.1.1:
pip install LFPsimpy-0.1.1.tar.gz