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.

v4.1.3 – Incremental Release over v4.1.0 "Jhangri"

Patch release focusing on accurate version reporting, bug fixes, and documentation improvements.

ABOUT VERSION 4.1.3, Jhangri

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:

Installation

Installing released version from PyPI using pip

This version is now available for installation via pip. To install the latest release, run

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

Fixes

1. Version mismatch resolved: moose.__version__ and moose.version() now correctly report 4.1.3 (previous 4.1.2 could still show 4.1.1).
2. Fixed vec index issue: Resolved an issue where accessing the last element in a vector could incorrectly raise an “index out of range” error.
3. Embedded C/C++ macro (MOOSE_VERSION) and build metadata are kept in sync with pyproject.toml.

Documentation

INSTALL.md rewritten for clearer, up-to-date installation instructions (PyPI and from-source code installation).

LICENSE

MOOSE is released under GPLv3.