https://github.com/russelljjarvis/bluepyoptbu
https://github.com/russelljjarvis/bluepyoptbu
Last synced: 4 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/russelljjarvis/bluepyoptbu
- Owner: russelljjarvis
- License: other
- Created: 2021-01-24T05:34:37.000Z (over 4 years ago)
- Default Branch: eliteism
- Last Pushed: 2021-01-24T05:35:49.000Z (over 4 years ago)
- Last Synced: 2025-01-10T03:18:51.857Z (5 months ago)
- Language: Python
- Size: 77.7 MB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
- Authors: AUTHORS.txt
Awesome Lists containing this project
README
[](https://travis-ci.org/BlueBrain/BluePyOpt)
[](https://codecov.io/github/BlueBrain/BluePyOpt?branch=master)
[](https://gitter.im/BlueBrain/BluePyOpt?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
[](https://codeclimate.com/github/BlueBrain/BluePyOpt)
[](http://bluepyopt.readthedocs.io/en/latest/?badge=latest)
Introduction
============
The Blue Brain Python Optimisation Library (BluePyOpt) is an extensible
framework for data-driven model parameter optimisation that wraps and
standardises several existing open-source tools.
It simplifies the task of creating and sharing these optimisations,
and the associated techniques and knowledge.
This is achieved by abstracting the optimisation and evaluation tasks
into various reusable and flexible discrete elements according to established
best-practices.
Further, BluePyOpt provides methods for setting up both small- and large-scale
optimisations on a variety of platforms,
ranging from laptops to Linux clusters and cloud-based compute infrastructures.
Citation
========
When you use the BluePyOpt software or method for your research, we ask you to cite the following publication:
[Van Geit W, Gevaert M, Chindemi G, Rössert C, Courcol J, Muller EB, Schürmann F, Segev I and Markram H (2016). BluePyOpt: Leveraging open source software and cloud infrastructure to optimise model parameters in neuroscience. Front. Neuroinform. 10:17. doi: 10.3389/fninf.2016.00017](http://journal.frontiersin.org/article/10.3389/fninf.2016.00017)
```bibtex
@ARTICLE{bluepyopt,
AUTHOR={Van Geit, Werner and Gevaert, Michael and Chindemi, Giuseppe and Rössert, Christian and Courcol, Jean-Denis and Muller, Eilif Benjamin and Schürmann, Felix and Segev, Idan and Markram, Henry},
TITLE={BluePyOpt: Leveraging open source software and cloud infrastructure to optimise model parameters in neuroscience},
JOURNAL={Frontiers in Neuroinformatics},
VOLUME={10},
YEAR={2016},
NUMBER={17},
URL={http://www.frontiersin.org/neuroinformatics/10.3389/fninf.2016.00017/abstract},
DOI={10.3389/fninf.2016.00017},
ISSN={1662-5196}
}
```
Support
=======
We are providing support using a chat channel on [Gitter](https://gitter.im/BlueBrain/BluePyOpt).
News
====
- 2017/01/04: BluePyOpt is now considered compatible with Python 3.6+.
- 2016/11/10: BluePyOpt now supports NEURON point processes. This means we can fit parameters of Adex/GIF/Izhikevich models, and also synapse models.
- 2016/06/14: Started a wiki: https://github.com/BlueBrain/BluePyOpt/wiki
- 2016/06/07: The BluePyOpt paper was published in Frontiers in Neuroinformatics (for link, see above)
- 2016/05/03: The API documentation was moved to [ReadTheDocs](http://bluepyopt.readthedocs.io/en/latest/)
- 2016/04/20: BluePyOpt now contains the code of the IBEA selector, no need to install a BBP-specific version of DEAP anymore
- 2016/03/24: Released version 1.0
Requirements
============
* [Python 2.7+](https://www.python.org/download/releases/2.7/) or [Python 3.6+](https://www.python.org/downloads/release/python-360/)
* [Pip](https://pip.pypa.io) (installed by default in newer versions of Python)
* [Neuron 7.4](http://neuron.yale.edu/) (compiled with Python support)
* [eFEL eFeature Extraction Library](https://github.com/BlueBrain/eFEL) (automatically installed by pip)
* [Numpy](http://www.numpy.org) (automatically installed by pip)
* [Pandas](http://pandas.pydata.org/) (automatically installed by pip)
* The instruction below are written assuming you have access to a command shell
on Linux / UNIX / MacOSX / Cygwin
Installation
============
If you want to use the ephys module of BluePyOpt, you first need to install Neuron with Python support on your machine.
And then bluepyopt itself:
```bash
pip install bluepyopt
```
Cloud infrastructure
====================
We provide instructions on how to set up an optimisation environment on cloud
infrastructure or cluster computers
[here](https://github.com/BlueBrain/BluePyOpt/tree/master/cloud-config)
Quick Start
===========
Single compartmental model
--------------------------
An iPython notebook with an introductory optimisation of a one compartmental
model with 2 HH channels can be found at
https://github.com/BlueBrain/BluePyOpt/blob/master/examples/simplecell/simplecell.ipynb
There is a Binder Virtual Machine available that allows you to run this notebook in your browser:
http://mybinder.org/repo/BlueBrain/BluePyOpt/examples/simplecell/simplecell.ipynb
**Figure**: The solution space of a single compartmental model with two parameters: the maximal conductance of Na and K ion channels. The color represents how well the model fits two objectives: when injected with two different currents, the model has to fire 1 and 4 action potential respectively during the stimuli. Dark blue is the best fitness. The blue circles represent solutions with a perfect score.
Neocortical Layer 5 Pyramidal Cell
----------------------------------
Scripts for a more complex neocortical L5PC are in
[this directory](https://github.com/BlueBrain/BluePyOpt/tree/master/examples/l5pc)
With a notebook:
https://github.com/BlueBrain/BluePyOpt/blob/master/examples/l5pc/L5PC.ipynb
And you can run this in a VM:
http://mybinder.org/repo/BlueBrain/BluePyOpt/examples/l5pc/L5PC.ipynb
API documentation
==================
The API documentation can be found on [ReadTheDocs](http://bluepyopt.readthedocs.io/en/latest/).
Funding
=======
This work has been partially funded by the European Union Seventh Framework Program (FP7/20072013) under grant agreement no. 604102 (HBP)