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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.
When you use the BluePyOpt software or method for your research, we ask you to cite the following publication (this includes poster presentations):
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>_.
.. code-block::
@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}
}
The list of publications that use or mention BluePyOpt can be found on the github wiki page <https://github.com/BlueBrain/BluePyOpt/wiki/Publications-that-use-or-mention-BluePyOpt>_.
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>.
ReadTheDocs <http://bluepyopt.readthedocs.io/en/latest/>_Python 3.9+ <https://www.python.org/downloads/release/python-390/>_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)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:
.. code-block:: bash
pip install bluepyopt
Support for simulators other than NEURON is optional and not installed by default. If you want to use Arbor to run your models, use the following line instead to install bluepyopt.
.. code-block:: bash
pip install bluepyopt[arbor]
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>_
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 (NEURON) https://github.com/BlueBrain/BluePyOpt/blob/master/examples/simplecell/simplecell_arbor.ipynb (Arbor)
|landscape_example|
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.
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 (NEURON) https://github.com/BlueBrain/BluePyOpt/blob/master/examples/l5pc/L5PC_arbor.ipynb (Arbor)
Scripts for 2 thalamocortical cell types are in
this directory <https://github.com/BlueBrain/BluePyOpt/tree/master/examples/thalamocortical-cell>__
With a notebook:
Scripts for 2 version of fitting the Tsodyks-Markram model to synaptic traces are in
this directory <https://github.com/BlueBrain/BluePyOpt/tree/master/examples/tsodyksmarkramstp>__
With 2 notebooks:
https://github.com/BlueBrain/BluePyOpt/blob/master/examples/tsodyksmarkramstp/tsodyksmarkramstp.ipynb https://github.com/BlueBrain/BluePyOpt/blob/master/examples/tsodyksmarkramstp/tsodyksmarkramstp_multiplefreqs.ipynb
An iPython notebook showing how to export a BluePyOpt cell in the neuroml format, how to create a LEMS simulation, and how to run the LEMS simulation with the neuroml cell can be found at:
https://github.com/BlueBrain/BluePyOpt/blob/master/examples/neuroml/neuroml.ipynb
The API documentation can be found on ReadTheDocs <http://bluepyopt.readthedocs.io/en/latest/>_.
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). 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.
Copyright (c) 2016-2024 Blue Brain Project/EPFL
.. The following image is also defined in the index.rst file, as the relative path is different, depending from where it is sourced. The following location is used for the github README The index.rst location is used for the docs README; index.rst also defined an end-marker, to skip content after the marker 'substitutions'.
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.. substitutions .. |banner| image:: docs/source/logo/BluePyOptBanner.png .. |landscape_example| image:: examples/simplecell/figures/landscape_example.png
FAQs
Blue Brain Python Optimisation Library (bluepyopt)
We found that bluepyopt demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 4 open source maintainers collaborating on the project.
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