What is MOOSE and what is it good for?
-------------------------------------

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.

.. figure:: ../images/Gallery_Moose_Multiscale.png
   :scale: 75%
   :alt: **multiple scales in moose**

   *Multiple scales can be modelled and simulated in MOOSE*

MOOSE is multiscale: It can do all these calculations together. One of its major
uses is to make biologically detailed models that combine 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 numerical 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.

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