20 November 2015

MOOSE runs with graphics on the Mac
Welcome to 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 a simulation environment, not just a numerical engine. It provides the essentials by way of object-oriented representations of model concepts and fast numerical solvers, but its scope is much broader.
It has a scripting interface with Python, graphical displays with Matplotlib, PyQt, and OpenGL, and support for many model formats.

MOOSE can read kinetic models in SBML and GENESIS kkit formats, from and DOQCS. MOOSE also supports electrical models specified in NeuroML and Genesis .p formats, and can load over 30,000 morphology files from (.swc format).

About MOOSE version 3.0.2pre, Ghevar

The Ghevar release is the third of series 3 of MOOSE releases.

Ghevar is a Rajasthani sweet with a stiff porous body soaked in sugar syrup.

MOOSE 3.0.2 is an evolutionary increment over 3.0.1:

  - There has been substantial development on the multiscale modeling front, with the implementation of the rdesigneur class and affiliated features.
  - Distributions of spines, channels, physiological parameters, chemical concentrations along the cell can be specified using algebraic functions of geometrical properties of the cell.
  - MOOSE can now read NeuroMorpho .swc files natively.
  - MOOSE can now write natively to the Neuronal Simulation Data Format. The NSDF is based on HDF5 and is a portable and self-descriptive way to store simulation output.

Source code
Building blocks
Moose can read NeuroML and SWC files
This LIF network with Ca plasticity is based on: David Higgins, Michael Graupner, Nicolas Brunel Memory Maintenance in Synapses with Calcium-Based Plasticity in the Presence of Background Activity PLOS Computational Biology, 2014 Image
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