Neurodamus
Neurodamus is a BBP Simulation Control application for Neuron.
The Python implementation offers a comprehensive Python API for fine tuning of the simulation, initially defined by a BlueConfig file.
Description
Neurodamus is the BBP in-house developed application for setting up large-scale neuronal simulations. It has traditionally been implemented as a set of extensions to Neuron, in the form of .hoc and .mod files. The parameters of the simulation are loaded from a configuration file, by default BlueConfig.
To address several limitations of the Hoc implementation, including development effort, the high-level layers of Neurodamus have been reimplemented in Python. Such implementation effectively makes available to the user a Python module with a comprehensive API, suitable to fine control simulation aspects, as well as inspect and eventually adapt the simulations as intended.
Install
Prerequisites
hdf5
libsonatareport https://github.com/openbraininstitute/libsonatareport
neuron with MPI https://github.com/neuronsimulator/nrn
Install neurodamus
git clone https://github.com/openbraininstitute/neurodamus.git
cd neurodamus
pip install .Build special with mod files
Once neuron and neurodamus are installed, you can build special with your mod files:
mkdir mods
cp -r <your-mod-files> mods/
export DATADIR=$(python -c "import neurodamus; from pathlib import Path; print(Path(neurod)mus.__file__).parent / 'data')")
cp -L $DATADIR/mod/* mods/
nrnivmodl -incflags '-I <include-paths-of-our-dependencies>' -loadflags '-L <libs-paths-for-linking>' modsTo use the Blue Brain open models, you can build neurodamus-models rel 2.4.4. It will also produce a handy build_neurodamus.sh script that calls nrnivmodl with all dependencies to compile your future mod files
build_neurodamus mods/Examples
Once neurodamus is installed, you should be able to find the executable neurodamus in your path:
$ neurodamus
Usage:
neurodamus <ConfigFile> [options]
neurodamus --helpAmong the options you will find flags to tune run behavior.
Neurodamus explicitly depends on MPI libraries for parallel execution. Therefore please use “srun” or “mpiexec” to launch it, according to your platform. If you don’t, complicated error messages may show up. Please remember it.
Even though a neurodamus launcher is provided, for production runs we suggest using special instead. This way has proven to take advantage of optimized math libraries. We hope to bring the same advantages to the launcher script soon.
export NEURODAMUS_PYTHON=$(python -c "import neurodamus; from pathlib import Path; print(Path(neurod)mus.__file__).parent / 'data')")
export HOC_LIBRARY_PATH=<hoc_files_folder>
srun <srun params> <your_built_special> -mpi -python $NEURODAMUS_PYTHON/init.py <neurodamus params>An example of a full installation with a simulation run can be found in the workflow test simulation_test.yml.
Docker container
Alternatively, you can start directly a neurodamus docker container where all the packages are built. With the container, you can build your mod files and run simulations.
Building the docker container
The docker images will be built in the regular github action - it is built on tags.
Docker container
With the docker image, you can start a neurodamus container with an interactive Bash shell and meanwhile mount your local folder which contains your mod files and the circuit data.
docker run --rm -it -v <full path of your folder_mods_circuit>:/mnt/mydata ghcr.io/openbraininstitute/neurodamus:$TAG /bin/bashTo build new models, one can use the script in /opt/obi/build-neurodamus-models.sh. One can also examine the /opt/obi/env-neocortex.sh file to see how neocortext environment variables are setup so that the models are loaded. NEURON is installed, so by activating the virtual environment with source $USER_VENV/bin/activate one gets access to neuron and neurodamus
Acknowledgment
The development of this software 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) 2005-2024 Blue Brain Project/EPFL
Copyright (c) 2025-2026 Open Brain Institute
3 maintainers
Metadata
3 maintainers
Release files for neurodamus 4.2.8
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| neurodamus-4.2.8.tar.gz | 202.2 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| neurodamus-4.2.8-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 439.7 kB
Release files / neurodamus-4.2.8.tar.gz
| Download URL | neurodamus-4.2.8.tar.gz |
|---|---|
| Size | 202.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
095a1d78e967e323b71b2d836832c20cf3b19d3afefacfb8e66d0dc27115ad2d
|
|
BLAKE2b-256 checksum How to use checksums |
80e08a8c82c1375c3c8dd449d82d6fd9e3bc936906568d3349f2c65954cb6844
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 14, 2026.
Transparency logRelease files / neurodamus-4.2.8-py3-none-any.whl
| Download URL | neurodamus-4.2.8-py3-none-any.whl |
|---|---|
| Size | 237.5 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
eb6d90a0c581cbb58d053710aecfae78b62b62b7da57d95a5a1601998d28b16b
|
|
BLAKE2b-256 checksum How to use checksums |
d6ac9744a49d1c9a437a8158b3579fa746df0a3e765888c7cabb6c4c08a2a8c9
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 14, 2026.
Transparency log
