The Silver Lab
UCL logo

Neuroinformatics combines computer science, software development and modelling of neural systems. Our lab has developed and maintained the following resources and software packages...


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 To see more neuroConstruct images and movies click here.


Network models are often inaccessible because of the number of distinct programming languages and specialist simulators used (e.g. NEURON, GENESIS, MOOSE and NEST). This also prevents model sharing. Recently we have taken a leading role in an international project to design NeuroML, a new XML-based model description language for computational neuroscience. This standardized language can describe kinetic models of channels, synaptic dynamics, complex cell morphologies as well as network connectivity and gross anatomy. This allows biologically detailed single cell and network models to be defined and stored in a simulator-independent format allowing greater interoperability, accessibility and transparency. We have recently released the first full version of NeuroML and published a paper describing its structure (Gleeson et al. 2010). NeuroML is already being used in high impact applications such as the Whole Brian Catalogue to define neuronal morphologies, which also uses neuroConstruct to run simulations. Neuronal and network models defined in NeuroML and the latest developments towards NeuroML version 2.0 can be found on

Open Source Brain

The Open Source Brain repository (OSB) aims to be a public repository for detailed neuronal models in standardised formats, with curated, stable releases which will evolve in line with new experimental findings, the latest modelling paradigms and simulator technology development. While the models can be collaboratively developed in any simulator format, the ultimate aim is to get as much of the model as possible into NeuroML format to ensure modularity, accessibility and cross simulator portability.


NeuroMatic is a widely used suite of functions in the IGOR PRO environment for electrophysiological data acquisition and analysis. NeuroMatic is platform independent (Mac or PC) and works with InstruTech/Heka or NIDAQ acquisition hardware. NeuroMatic is freely available and has been downloaded by more than 1000 scientists around the world.

pymuvr is a high performance Python package for the calculation of multi-unit Van Rossum neural spike train distances and inner products. It is built around a hand-optimised C++ implementaton of the kernel-based algorithm by Houghton and Kreuz (2012).

This page last modified 20 November 2012
University College London - Gower Street - London - WC1E 6BT - Telephone: +44 (0)20 7679 2013 - Copyright © 1999-2011 UCL
Disclaimer | Accessibility | Privacy | Advanced Search | Help

Search by Google