Introduction

neuroConstruct is being developed in the Silver Lab in the Department of Neuroscience, Physiology and Pharmacology at UCL. neuroConstruct has been designed to simplify development of complex networks of biologically realistic neurons, i.e. models incorporating dendritic morphologies and realistic cell membrane conductances. It is implemented in Java and generates script files for the NEURON and GENESIS simulators, with support for other simulation platforms (including PSICS, MOOSE and PyNN) in advanced stages of development. It uses the latest NeuroML specifications, including MorphML, ChannelML and NetworkML.

Development of this software was made possible with funding from the Wellcome Trust, the Medical Research Council and the EU Synapse Project.

Latest News

A recent Nature paper (Jason S. Rothman, Laurence Cathala, Volker Steuber, R. Angus Silver, Synaptic depression enables neuronal gain control. Nature 2009) has used neuroConstruct to investigate a detailed layer 5 pyramidal cell model (Kole et al 2008) with dendritically distributed excitatory and inhibitory synaptic input to look at the effects of short term plasticity on gain control (Download neuroConstruct project).

The latest version of neuroConstruct (v1.3.6, Jan 2010) is available for download here. The source code is available with this release under GPL.

The paper describing the application has been published: P. Gleeson, V. Steuber and R. A. Silver, neuroConstruct: A Tool for Modeling Networks of Neurons in 3D Space, Neuron, Volume 54, Issue 2, 19 April 2007, Pages 219-235. It is available via Open Access here.

A more complete list of publications dealing with neuroConstruct and NeuroML can be found here.

There is a mailing list for neuroConstruct related news (neuroconstruct@ucl.ac.uk). Sign up here.

What is neuroConstruct?

Some of the key features of neuroConstruct are:

• neuroConstruct can import morphology files in GENESIS, NEURON, Neurolucida, SWC and MorphML format for inclusion in single cell or network models, or more abstract cells can also be built manually.
• Creation of networks of conductance based neurons positioned in 3D
• Complex connectivity patterns between cell groups can be specified for the networks
• Simulation scripts can be generated for NEURON, GENESIS, MOOSE, PSICS and PyNN based simulators (note: not every project can be generated for every simulator)
• Biophysically realistic cellular mechanisms (synapses/channel mechanisms) can be imported from native script files (*.mod or *.g) or created from templates using ChannelML
• Automatic generation of code to record simulation data and visualisation/analysis of data in neuroConstruct
• Recorded simulation runs can be viewed and managed through the Simulation Browser interface
• A Python based scripting interface can be used to control model generation and execution, allowing multiple simulations to be run for cell and network model optimisation

What isn't neuroConstruct?

• neuroConstruct is not a replacement for neural network simulators like NEURON or GENESIS

These packages have sophisticated environments for creating neuronal simulations from scratch, and controlling all aspects of the simulation run. neuroConstruct automates the generation of script files for these platforms, and provides a framework for creating conductance based neuronal models, creating, visualising and analysing networks of cells in 3D, managing simualtions and analysing network firing behaviour.