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 .

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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.

v4.2.0 – Major Release "Kalakand"

Kalakand is a popular Indian sweet made from solidified sweetened milk and cottage cheese (paneer), with a soft, grainy texture and a rich, mildly sweet flavour. It is often garnished with cardamom and pistachios and is a favourite at festivals and celebrations across India.

What's New in 4.2.0

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

Breaking Changes

• Some legacy and unused Python utility modules have been removed. If your scripts import from moose.recording, moose.constants, or moose.method_utils, you will need to update them.
• getFieldDict has been renamed to getFieldTypeDict. If your scripts use this function, update the name accordingly.

Neuron Morphology (SWC) Improvements

• Improved support for loading neuron morphologies: SWC files with 2-point soma (as used by Arbor) and 3-point soma formats are now handled correctly
• Automated SWC compartmentalization using uniform RA and RM based on ShapeShifter
• Added a dedicated moose.loadSwc() function for loading SWC files with optional electrical parameters (RM, RA, CM)

Model Loading Improvements

• Added explicit moose.loadKkit() function for loading GENESIS Kkit models
• NeuroML2 model path is now configurable instead of being hardcoded

Python Interface Improvements

• Consistent and informative string representation for all MOOSE Python objects, making debugging and interactive use easier
• getFieldNames() is now available as a method in MOOSE objects

Bug Fixes

• Fixed incorrect behaviour when setting attributes on element fields via Python
• Fixed an intermittent issue where expression evaluation could fail unpredictably under certain conditions
• Fixed missing runtime dependencies for NeuroML2 module (pint, scipy)

Build and Packaging

• Python bindings rebuilt on nanobind, replacing pybind11, resulting in faster and smaller code
• Building MOOSE from source is now simpler, with fewer manual setup steps required
• Updated CI workflows for the new build system

LICENSE

MOOSE is released under GPLv3.