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 .
See docs/source/install/INSTALL.md for instructions on installation.
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Have a look at examples, tutorials and demo scripts here https://github.com/MooseNeuro/moose-examples.
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A set of jupyter notebooks with step by step examples with explanation are available here: https://github.com/MooseNeuro/moose-notebooks.
Patch release focusing on accurate version reporting, bug fixes, and documentation improvements.
Jhangri is an Indian sweet
in the shape of a flower. It is made of white-lentil (Vigna mungo)
batter, deep-fried in ornamental shape to form the crunchy, golden
body, which is then soaked in sugar syrup lightly flavoured with
spices.
This release has the following changes:
Installing released version from PyPI using pip
This version is available for installation via pip. To install the latest release, run
conda create -n moose python=3.13 gsl hdf5 numpy vpython matplotlib -c conda-forge
conda activate moose
pip install pymoose
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
- Fixed a crash (segmentation fault) that could occur when deleting function objects
- Fixed incorrect evaluation order in function objects that could lead to wrong results in some models
- Improved stability of expression parsing when working with dynamically changing expressions
- Fixed setNumVar issue in Function class - setting the number of x variables with
numVarfield is no longer required, simply updating
the expression now works correctly
- Improved SWC morphology reader with clearer hierarchical naming scheme for dendritic compartments, making imported neuron structures easier to interpret and debug
- Updated build instructions for macOS
- Improved GitHub Actions workflows for release packages
- Enabled manual triggering of release workflows
- Fixed permission issues during GitHub release creation
MOOSE is released under GPLv3.