AUTHOR=Hull Michael J., Willshaw David J.
TITLE=morphforge: a toolbox for simulating small networks of biologically detailed neurons in Python
JOURNAL=Frontiers in Neuroinformatics
VOLUME=Volume 7 - 2013
YEAR=2014
URL=https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2013.00047
DOI=10.3389/fninf.2013.00047
ISSN=1662-5196
ABSTRACT=The broad structure of a modelling study can often be explained over a cup of coffee, but
converting this high-level conceptual idea into graphs of the final simulation results may require
many weeks of sitting at a computer. Although models themselves can be complex, often
many mental resources are wasted working around complexities of the software ecosystem
such as fighting to manage files, interfacing between tools and data formats, finding mistakes
in code or working out the units of variables. morphforge is a high-level, Python toolbox
for building and managing simulations of small populations of multicompartmental biophysical
model neurons. An entire in silico experiment, including the definition of neuronal morphologies,
channel descriptions, stimuli, visualisation and analysis of results can be written within a single
short Python script using high-level objects. Multiple independent simulations can be created
and run from a single script, allowing parameter spaces to be investigated. Consideration has
been given to the reuse of both algorithmic and parameterisable components to allow both
specific and stochastic parameter variations. Some other features of the toolbox include: the
automatic generation of human-readable documentation (e. g. PDF-files) about a simulation; the
transparent handling of different biophysical units; a novel mechanism for plotting simulation
results based on a system of tags; and an architecture that supports both the use of established
formats for defining channels and synapses (e. g. MODL files), and the possibility to support
other libraries and standards easily. We hope that this toolbox will allow scientists to quickly
build simulations of multicompartmental model neurons for research and serve as a platform for
further tool development.