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fit_neuron-0.0.6
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مشاهده بیشترتوضیحات
Package for estimation and evaluation of neural models from patch clamp neural recordings.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | fit_neuron-0.0.6 |
| نام | fit_neuron |
| نسخه کتابخانه | 0.0.6 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Nicolas D. Jimenez |
| ایمیل نویسنده | nicodjimenez@gmail.com |
| آدرس صفحه اصلی | http://pythonhosted.org/fit_neuron |
| آدرس اینترنتی | https://pypi.org/project/fit_neuron/ |
| مجوز | Apache |
=============
fit_neuron
=============
**fit_neuron** is an easy to use python package for the fast estimation of generalized integrate and fire neural models
from patch clamp electrophysiological recordings. The optimization routines implements a fitting procedure
described in [RB2005]_ and [MS2011]_. The package includes an easy to use interface similar to scikit-learn for fitting models to data and then making predictions with the fitted models. The routines used can estimate the models described in [RB2005]_, [MN2009]_, and [MS2011]_.
As described in depth in the documentation, the subthreshold
parameters are estimated using linear regression and the threshold parameters are estimated
using maximum likelihood. The fitting routine is built for speed: it estimates neuron parameters for 10 seconds of data
in about 50 seconds on a quad core Asus laptop. *fit_neuron* also contains efficient implementations
of the following spike distance measures: Victor-Purpura [DA2003]_, van Rossum [VR2001]_, Schreiber [SS2003]_, and Gamma [RJ2008]_
which can be used to evaluate the accuracy of estimated models, as well as provide measures
of synchrony between spike trains.
:Date: 2013-12-28
:Version: 0.0.5
:Authors: - Nicolas D. Jimenez
Links
----------
1) **Pypi**
The latest stable version is available to download at: https://pypi.python.org/pypi/fit_neuron.
2) **GitHub**
The latest development version is available at: https://github.com/nicodjimenez/fit_neuron. All relevant contributions are welcome
and fast review of pull requests is guaranteed.
3) **Documentation**
Sphinx documentation for this package is available at: http://pythonhosted.org/fit_neuron/.
Dependencies
-------------
1) **Numpy**
The standard python module for matrix and vector computations: https://pypi.python.org/pypi/numpy.
2) **Scipy**
The standard python module for statistical analysis: http://www.scipy.org/install.html.
3) **Matplotlib**
The standard python module for data visualization: http://matplotlib.org/users/installing.html.
Installation
-----------------------
The fit_neuron package can be installed as follows::
sudo pip install fit_neuron
The data for the fit_neuron package is then installed as follows::
sudo python -m fit_neuron.data.dl_neuron_data
.. warning::
Running this script for the first time will download a 300 MB zip file containing test recordings
which is then unzipped to over 1 GB of text files in the installation directory of the *fit_neuron*
package. This may take up to 20 minutes depending on your bandwidth. After the files are downloaded, the test
data will be easily accessible via the *fit_neuron.data* package.
Testing
------------
There are two testing scripts that may be used. Both scripts are
described in the documentation (http://pythonhosted.org/fit_neuron/).
The first script is far simpler and easier to understand but is less configurable::
python -m fit_neuron.tests.test_model
The more complicated and configurable testing script for fit_neuron can be run as follows::
python -m fit_neuron.tests.test
This will create a directory called *test_output_figures* in the current directory.
Feel free to contact me at nicodjimenez [at] gmail.com if you have any questions / comments.
References
------------------
.. [RB2005] Brette, Romain, and Wulfram Gerstner. "Adaptive exponential integrate-and-fire model as an effective description of neuronal activity."
Journal of neurophysiology 94.5 (2005): 3637-3642.
.. [MN2009] Mihalas, Stefan, and Ernst Niebur. "A generalized linear integrate-and-fire neural model produces diverse spiking behaviors."
Neural computation 21.3 (2009): 704-718.
.. [MS2011] Mensi, Skander, et al. "Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms."
Journal of neurophysiology 107.6 (2012): 1756-1775.
.. [RJ2008] Jolivet, Renaud, et al. "A benchmark test for a quantitative assessment of simple neuron models."
Journal of neuroscience methods 169.2 (2008): 417-424.
.. [SS2003] Schreiber, S., et al. "A new correlation-based measure of spike timing reliability."
Neurocomputing 52 (2003): 925-931.
.. [VR2001] van Rossum, Mark CW. "A novel spike distance."
Neural Computation 13.4 (2001): 751-763.
.. [DA2003] Aronov, Dmitriy. "Fast algorithm for the metric-space analysis
of simultaneous responses of multiple single neurons." Journal
of Neuroscience Methods 124.2 (2003): 175-179.
نحوه نصب
نصب پکیج whl fit_neuron-0.0.6:
pip install fit_neuron-0.0.6.whl
نصب پکیج tar.gz fit_neuron-0.0.6:
pip install fit_neuron-0.0.6.tar.gz