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This is the parent repository of [MOOSE simulator](https://moose.ncbs.res.in).
It keeps the snanshots of its components: core (written in C++) and python
scripting interface, MOOSE's Graphical User Interface (GUI). This should be the
source of stable MOOSE code.
Dilawar Singh
committed
MOOSE is the Multiscale Object-Oriented Simulation Environment. It is designed
to simulate neural systems ranging from biochemical signaling to complex models
of single neurons, circuits, and large networks. A typical use case is to model
neural activity-driven synaptic plasticity, in which network activity to
synapses triggers biochemical reaction-diffusion events, which in turn modulate
ion channels to give rise to synaptic plasticity.
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 OpenGL, and
support for many model formats. These include SBML, NeuroML, GENESIS kkit and
cell.p formats, HDF5 and NSDF for data writing.
# Documentation
MOOSE Documentation can be found at https://moose.ncbs.res.in/?q=documentation .
MOOSE 3.1 (current version 3.1.2) is an evolutionary update. It has a new
Python-based SBML reader , improved handling of reaction-diffusion systems, and
substantial refinements to the rdesigneur interface for setting up multiscale
models. Several additional rdesigneur tutorials have been implemented.
Look in the [moose-examples repository](https://github.com/BhallaLab/moose-examples) for sample code.
- [tutorials](https://github.com/BhallaLab/moose-examples/tree/master/tutorials): Standalone scripts meant for teaching. Students are expected
to modify the scripts to learn the principles of the models.
- [squid](https://github.com/BhallaLab/moose-examples/tree/master/squid): The Hodkin-Huxley squid model, fully graphical interface.
- [Genesis_files](https://github.com/BhallaLab/moose-examples/tree/master/genesis): A number of kinetics models used in MOOSE demos.
- [neuroml](https://github.com/BhallaLab/moose-examples/tree/master/neuroml): A number of NeuroML models used in MOOSE demos
- [traub_2005](https://github.com/BhallaLab/moose-examples/tree/master/traub_2005): Example scripts for each of the individual cell models from
the Traub 2005 thalamocortical model.
- [snippets](https://github.com/BhallaLab/moose-examples/tree/master/snippets): Code snippets that can be used as building blocks and to
illustrate how to use certain kinds of objects in MOOSE. These snippets are
all meant to run as individual files.
MOOSE comes with a NeuroML reader. Demos/neuroml has some python scripts showing
how to load NeuroML models.
MOOSE is backward compatible with GENESIS kinetikit. Demos/Genesis_files has
some examples. You can load a kinetikit model with the loadModel function:
moose.loadModel(kkit_file_path, modelname )
MOOSE is backward compatible with GENESIS <model>.p files used for neuronal
model specification. The same loadModel function can be used for this but you
need to have all the channels used in the .p file preloaded in /library:
moose.loadModel(prototype_file_path, modelname )
MOOSE is released under the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or (at
your option) any later version.