Biologically detailed network models are a powerful way
to bridge the considerable gap in our understanding between
low level mechanisms and high level network function.
Unfortunately, models that incorporate cell morphologies, synaptic connectivity patterns
and synaptic and neuronal properties are difficult to build.
To overcome this problem we have developed
neuroConstruct,
a software tool for constructing, visualizing and analyzing
conductance-based neural network models in 3D space
(
Gleeson et al. 2007).
This application allows the development of models
with a much higher degree of biological detail than was previously possible
(see image below).
By automatically generating and managing the code of these complex models
it also facilitates the exploration of parameter space
through the generation of many realizations of a particular model.
We have used neuroConstruct to investigate the effects of synaptic short term plasticity
on gain control in a detailed layer 5 pyramidal cell model
with dendritically distributed excitatory and inhibitory synaptic input
(
Rothman et al. 2009).
The latest version has a python interface for added flexibility
and large-scale network models can now be set up
and automatically managed on parallel computer architectures,
features that we recently used to investigate the effects of electrical coupling
on the desynchronization of an electrically coupled network of cerebellar Golgi cells
(
Vervaeke et al. 2010).
Since its release neuroConstruct has been downloaded by over
1100 registered users in 40 different countries worldwide.
neuroConstuct software and models built with this application are freely available from
www.neuroConstuct.org.
To see more neuroConstruct images and movies click
here.
