MOOSE
MOOSE is the Multiscale Object-Oriented Simulation Environment. It is designed to simulate neural systems ranging from subcellular components and biochemical reactions to complex models of single neurons, circuits, and large networks. MOOSE can operate at many levels of detail, from stochastic chemical computations, to multicompartment single-neuron models, to spiking neuron network models.
MOOSE is multiscale: It can do all these calculations together. For example it handles interactions seamlessly between electrical and chemical signaling. MOOSE is object-oriented. Biological concepts are mapped into classes, and a model is built by creating instances of these classes and connecting them by messages. MOOSE also has classes whose job is to take over difficult computations in a certain domain, and do them fast. There are such solver classes for stochastic and deterministic chemistry, for diffusion, and for multicompartment neuronal models.
MOOSE is a simulation environment, not just a numerical engine: It provides data representations and solvers (of course!), but also a scripting interface with Python, graphical displays with Matplotlib, PyQt, and VPython, and support for many model formats. These include SBML, NeuroML, GENESIS kkit and cell.p formats, HDF5 and NSDF for data writing.
This is the core computational engine of MOOSE
simulator. This repository
contains C++ codebase and python interface called pymoose. For more
details about MOOSE simulator, visit https://moose.ncbs.res.in .
Installation
See docs/source/install/INSTALL.md for instructions on installation.
Examples and Tutorials
- Have a look at examples, tutorials and demo scripts here https://github.com/MooseNeuro/moose-examples.
- A set of jupyter notebooks with step by step examples with explanation are available here: https://github.com/MooseNeuro/moose-notebooks.
v5.0.0 – Major Release "Mysore Pak"
Mysore Pak is a rich,
ghee-based sweet that originated in the kitchens of the Mysore Palace in
Karnataka, India. Made from gram flour (besan), ghee, and sugar syrup, it
is traditionally made in two textures a dense, fudge-like version and a
lighter, porous, melt-in-the-mouth version - depending on how the mixture
is aerated during cooking.
Quick Install
Installing released version from PyPI using pip
This version is available for installation via pip. To install the
latest release, we recommend creating a separate environment using
conda, mamba, micromamba, or miniforge to manage dependencies cleanly
and avoid conflicts with other Python packages. The conda-forge
channel has all the required libraries available for Linux, macOS,
and Windows.
conda create -n moose python=3.13 gsl hdf5 numpy vpython matplotlib -c conda-forge
conda activate moose
pip install pymoose
Post installation
You can check that moose is installed and initializes correctly by running:
$ python -c "import moose; ch = moose.HHChannel('ch'); moose.le()"
This should show
Elements under /
/Msgs
/clock
/classes
/postmaster
/ch
Now you can import moose in a Python script or interpreter with the statement:
>>> import moose
What's New in 5.0.0
Expanded NeuroML2 Support
MOOSE's NeuroML2 reader now correctly handles V and Ca2+-dependent 2D
channels (HHChannel2D) in NeuroML models. Custom ComponentType rate
formulas are now evaluated with exprtk instead of exec() with numpy,
along with a few minor fixes.
Docker-based Installation
MOOSE, JupyterLab, and JARDesigner (the web-based model-building GUI) are now available as a single, self-contained Docker image — no Python setup required, and it runs identically on Windows, macOS, and Linux. See moose-jardesigner-docker to get started.
Updates in 5.0.0
Breaking Changes
- moose.readSBML() and moose.writeSBML() now use the new reader/writer directly. They return a single model object and raise on failure, instead of the previous tuple return. moose.SBML.readSBML.mooseReadSBML is now deprecated
Improvements
- Replaced the SBML reader with a new, general-purpose one supporting a much wider range of standard model files
- Added support for loading and simulating SBML models with multiple compartments
- Added an SBML writer to the new reader/writer module, with broader support than the old legacy writer, and a round-trip with the reader verified to floating-point precision
- loadpath is now optional when loading an SBML model, defaulting to /library/{model_name}
- Added moose.NA, moose.FaradayConst, and other physical constants as directly accessible Python attributes
- Added a set of ready-to-use Allen Brain Database neuron morphologies to MOOSE's built-in library
- loadKkit() now shows a deprecation notice recommending loadModel()
- NeuroML2 ComponentType Dynamics rates are now evaluated with exprtk through a scratch moose.Function instead of exec() with numpy; expressions that cannot be translated raise UnsupportedMath
- modelpath is now optional in NML2Reader.read, defaulting to /model
- HHGate2D (2D-dependent gates) now supports specifying gate tables as alpha/beta or tau/inf expressions (alphaExpr/betaExpr/tauExpr/infExpr), the same way HHGate already does for 1D gates
Documentation
- Converged all MOOSE-authored source files to a uniform GNU GPLv3 license header
- Updated LICENSE file links to current gnu.org URLs
Bug Fixes
- Fixed Dsolve objects returning the wrong path they now correctly report their actual location in the model tree
- Fixed creating MOOSE objects with attributes passed as keyword arguments (e.g. moose.Pool('/x', concInit=9.99))
- Fixed creating an object at a path already holding a different type silently returning the wrong object instead of raising an error
- Fixed Function expressions with more than 10 input variables silently ignoring variables past the 10th
- Fixed SWC morphology files that number nodes starting from 0 (including real Allen Brain Database files) failing to load
- Fixed an inconsistent value used for the Nernst equation's R/F constant
- Fixed wildcardFind to correctly resolve . and .. in search paths
- Fixed import moose crashing when the optional pyneuroml package is not installed
- Fixed chemMerge failing on SBML files due to a call to a non-existent function
- Fixed the NeuroML2 temperature fallback returning 25 K instead of 298.15 K when a model specifies no temperature
- Fixed no vector<vector> field could be written from Python (e.g. assigning HHGate2D.tableA)
- Fixed a 2D gate lookup from Python crashing (e.g. HHGate2D.A([v, ca]))
- Rate ComponentTypes derived from NEURON .mod files now receive rateScale, the gate's Q10 factor
- Fixed a Case condition with a leading space causing NeuroML2 channels (e.g. Gran_NaF_98) to fail loading with IndentationError
- moose.readNML2 now takes filepath and an optional modelpath, instead of passing the single argument on as the file path
- Fixed Ubuntu installs failing to import moose due to the extension not carrying a runtime search path to its GSL dependency
- Fixed HHChannel2D/HHChannelF2D channels using a single-axis dependency mode (VOLT_INDEX/C1_INDEX/C2_INDEX) crashing
- Fixed a singularity check in HHGate's tau/inf expression evaluation that could miss near-zero values
Featured Libraries
Ion Channel Library
Access over 3,517 ion channel models from the
ICGenealogy database through the
moose.channels module. Supported ion classes include Na, K, Ca, KCa,
and IH. Insert channels into compartments using wildcards, lists, or
dictionaries, with support for distance-dependent conductance.
Channel metadata includes both modeldb_id (ModelDB reference) and
icg_id (unique ICGenealogy identifier) for precise channel identification.
Features:
- Search, info, and make_prototype accept
icg_idas an alternative tomodeldb_id - Simplified prototype naming format:
{suffix}_{modeldb_id} - New
get_icg_idfunction to retrieve ICG identifier for a channel
Morphology Library
The moose.morphologies module simplifies loading and working with
neuron morphologies. Load SWC files and access compartments via .root,
.soma, .compartments, and .select(pattern). Includes automatic
re-rooting of SWC files not rooted at soma.
Bundled morphologies from:
- Allen Cell Types Database
- Traub et al. 2005 thalamocortical network model
- Classic published literature
Credits and Citations
Ion Channel Library
The channel parameters and omnimodel formulation are the work of the ICGenealogy project and the Vogels group at IST Austria.
If you use moose.channels in your research, please cite:
Chintaluri, C., Podlaski, W., Bozelos, P. A., Gonçalves, P. J., Lueckmann, J.-M., Macke, J. H., & Vogels, T. P. (2025). An ion channel omnimodel for standardized biophysical neuron modelling. bioRxiv. https://doi.org/10.1101/2025.10.03.680368
and the IonChannelGenealogy database:
Podlaski, W. F., Seeholzer, A., Groschner, L. N., Miesenboeck, G., Ranjan, R., & Vogels, T. P. (2017). Mapping the function of neuronal ion channels in model and experiment. eLife, 6, e22152. https://doi.org/10.7554/eLife.22152
The ICG web application and channel specification sheets are available at: https://icg.neurotheory.ox.ac.uk/
Morphology Utilities (ShapeShifter)
Developed by Prof. Avrama Blackwell and her team, George Mason University. ShapeShifter: a morphology processing utility for compartmental neuron models. https://github.com/neurord/ShapeShifter
Used in:
moose.swc_utils,moose.morphologies(GENESIS.pfile support),python/moose/ShapeShifter/
If you use morphology conversion or reduction features in your research, please acknowledge Prof. Avrama Blackwell's group and the ShapeShifter project.
LICENSE
MOOSE is released under GPLv3.
2 maintainers
Metadata
2 maintainers
Release files for pymoose 5.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
Total release size: 175.8 MB
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.15
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Release files / pymoose-5.0.0-cp310-cp310-macosx_14_0_arm64.whl
| Download URL | pymoose-5.0.0-cp310-cp310-macosx_14_0_arm64.whl |
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| Size | 6.9 MB |
| Tags | CPython 3.10 macOS 14.0+ ARM64 |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.15
|
Release files / pymoose-5.0.0-cp39-cp39-win_amd64.whl
| Download URL | pymoose-5.0.0-cp39-cp39-win_amd64.whl |
|---|---|
| Size | 4.4 MB |
| Tags | CPython 3.9 Windows x86-64 |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.15
|
Release files / pymoose-5.0.0-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
| Download URL | pymoose-5.0.0-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl |
|---|---|
| Size | 9.4 MB |
| Tags | CPython 3.9 Linux glibc 2.27+ x86-64 Linux glibc 2.28+ x86-64 |
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SHA-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.15
|
Release files / pymoose-5.0.0-cp39-cp39-macosx_15_0_arm64.whl
| Download URL | pymoose-5.0.0-cp39-cp39-macosx_15_0_arm64.whl |
|---|---|
| Size | 7.0 MB |
| Tags | CPython 3.9 macOS 15.0+ ARM64 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.15
|
Release files / pymoose-5.0.0-cp39-cp39-macosx_14_0_arm64.whl
| Download URL | pymoose-5.0.0-cp39-cp39-macosx_14_0_arm64.whl |
|---|---|
| Size | 6.9 MB |
| Tags | CPython 3.9 macOS 14.0+ ARM64 |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.15
|
Release files / pymoose-5.0.0-cp38-cp38-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
| Download URL | pymoose-5.0.0-cp38-cp38-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl |
|---|---|
| Size | 9.4 MB |
| Tags | CPython 3.8 Linux glibc 2.27+ x86-64 Linux glibc 2.28+ x86-64 |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.13.15
|
