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When building a network simulation, biophysically detailed electrical models (e-models) need to be tested for every morphology that is possibly used in the circuit.
E-models can e.g. be obtained using BluePyOpt by data-driven model parameter optimisation. Developing e-models can take a lot of time and computing resources. Therefore, these models are not reoptimized for every morphology in the network. Instead we want to test if an existing e-model matches that particular morphology 'well enough'.
This process is called Cell Model Management (MM). It takes as input a morphology release, a circuit recipe and a set of e-models with some extra information. Next, it finds all possible (morphology, e-model)-combinations (me-combos) based on e-type, m-type, and layer as described by the circuit recipe, and calculates the scores for every combination. Finally, it writes out the resulting accepted me-combos to a database, and produces a report with information on the number of matches.
We are providing support using a chat channel on Gitter.
- Python 3.7+
- Neuron 7.4+
- eFEL eFeature Extraction Library
- BluePyOpt
- NumPy
- pandas
- matplotlib
- sh
- ipyparallel
- lxml
- h5py
- pyyaml
All of the requirements except for Neuron are automatically installed with bluepymm. The decision on how to install Neuron is left to the user.
One simple way of installing Neuron is through pip
pip install NEURONNeuron can also be installed from the source and used by bluepymm provided that it is compiled with Python support.
pip install bluepymmNOTES:
- Make sure you are using the latest version of pip (at least >9.0). Otherwise the ipython dependency will fail to install correctly.
- Make sure you are using a new version of git (at least >=1.8). Otherwise some exceptions might be raised by the versioneer module.
An IPython notebook with a simple test example can be found in:
https://github.com/BlueBrain/BluePyMM/blob/master/notebook/BluePyMM.ipynb
The API documentation can be found on ReadTheDocs.
BluePyMM is licensed under the LGPL, unless noted otherwise, e.g., for external dependencies. See file LGPL.txt for the full license.
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) 2016-2022 Blue Brain Project/EPFL