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This repository was archived by the owner on Feb 26, 2025. It is now read-only.
When you use the BluePyOpt software or method for your research, we ask you to cite the following publication (**this includes poster presentations**):
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[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)
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```bibtex
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@ARTICLE{bluepyopt,
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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},
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TITLE={BluePyOpt: Leveraging open source software and cloud infrastructure to optimise model parameters in neuroscience},
`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>`_.
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.. code-block::
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@ARTICLE{bluepyopt,
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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},
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TITLE={BluePyOpt: Leveraging open source software and cloud infrastructure to optimise model parameters in neuroscience},
We are providing support using a chat channel on [Gitter](https://gitter.im/BlueBrain/BluePyOpt), or the [Github discussion page](https://github.com/BlueBrain/BluePyOpt/discussions).
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We are providing support using a chat channel on `Gitter<https://gitter.im/BlueBrain/BluePyOpt>`_, or the `Github discussion page<https://github.com/BlueBrain/BluePyOpt/discussions>`_.
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News
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====
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- 2017/01/04: BluePyOpt is now considered compatible with Python 3.6+.
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- 2016/11/10: BluePyOpt now supports NEURON point processes. This means we can fit parameters of Adex/GIF/Izhikevich models, and also synapse models.
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- 2016/06/14: Started a wiki: https://github.com/BlueBrain/BluePyOpt/wiki
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- 2016/06/07: The BluePyOpt paper was published in Frontiers in Neuroinformatics (for link, see above)
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- 2016/05/03: The API documentation was moved to [ReadTheDocs](http://bluepyopt.readthedocs.io/en/latest/)
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- 2016/05/03: The API documentation was moved to `ReadTheDocs<http://bluepyopt.readthedocs.io/en/latest/>`_
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- 2016/04/20: BluePyOpt now contains the code of the IBEA selector, no need to install a BBP-specific version of DEAP anymore
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- 2016/03/24: Released version 1.0
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Requirements
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============
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*[Python 2.7+](https://www.python.org/download/releases/2.7/) or [Python 3.6+](https://www.python.org/downloads/release/python-360/)
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*[Pip](https://pip.pypa.io) (installed by default in newer versions of Python)
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*[Neuron 7.4+](http://neuron.yale.edu/) (compiled with Python support)
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*[eFEL eFeature Extraction Library](https://github.com/BlueBrain/eFEL) (automatically installed by pip)
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*[Numpy](http://www.numpy.org) (automatically installed by pip)
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*[Pandas](http://pandas.pydata.org/) (automatically installed by pip)
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* The instruction below are written assuming you have access to a command shell
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on Linux / UNIX / MacOSX / Cygwin
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* `Python 2.7+ <https://www.python.org/download/releases/2.7/>`_ or `Python 3.6+ <https://www.python.org/downloads/release/python-360/>`_
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* `Pip <https://pip.pypa.io>`_ (installed by default in newer versions of Python)
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* `Neuron 7.4+ <http://neuron.yale.edu/>`_ (compiled with Python support)
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* `eFEL eFeature Extraction Library` <https://github.com/BlueBrain/eFEL>`_ (automatically installed by pip)
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* `Numpy <http://www.numpy.org>`_ (automatically installed by pip)
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* `Pandas <http://pandas.pydata.org/>`_ (automatically installed by pip)
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* The instruction below are written assuming you have access to a command shell on Linux / UNIX / MacOSX / Cygwin
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Installation
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============
@@ -125,16 +129,18 @@ If you want to use the ephys module of BluePyOpt, you first need to install NEUR
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And then bluepyopt itself:
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```bash
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pip install bluepyopt
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```
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.. code-block:: bash
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pip install bluepyopt
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Cloud infrastructure
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====================
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We provide instructions on how to set up an optimisation environment on cloud
**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.
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Neocortical Layer 5 Pyramidal Cell
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----------------------------------
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Scripts for a more complex neocortical L5PC are in
The API documentation can be found on [ReadTheDocs](http://bluepyopt.readthedocs.io/en/latest/).
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=================
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The API documentation can be found on `ReadTheDocs<http://bluepyopt.readthedocs.io/en/latest/>`_.
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Funding
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=======
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This work has been partially funded by the European Union Seventh Framework Program (FP7/20072013) under grant agreement no. 604102 (HBP), the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 720270, 785907 (Human Brain Project SGA1/SGA2) and by the EBRAINS research infrastructure, funded from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3).
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This project/research was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.
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