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A thalamocortical network model in NeuroML

Padraig Gleeson1*, Matteo Farinella1, Guy O. Billings1 and Angus R. Silver1
  • 1 University College London, Department of Neuroscience, Physiology and Pharmacology, United Kingdom

NeuroML (http:/www.neuroml.org) is a language based on XML for expressing biophysically detailed neuronal network models. The most recent version of this language (Gleeson et al., 2010) can be used to express models of voltage and ligand gated ion channels, fixed and plastic synaptic mechanisms, complex neuronal morphologies with distributed conductances and network connectivity in 3D space.

NeuroML is being developed as part of an international initiative with the aim of making compartmental neuronal and network models more accessible, to allow such models to be validated across a wider range of simulators and to facilitate exchange and reuse of model components between researchers.

The network model developed by Traub et al., (2005) is one of the most advanced multicompartmental network models created to date. This model features principle cells and interneurons from multiple layers of the cortex and the thalamus connected according to anatomical data. We have converted all of the cell models from Traub et al. (2005) to NeuroML and have successfully reproduced the spiking behaviour of the cells across the neuronal simulators NEURON, GENESIS and MOOSE. We have also created a Layer 2/3 network model based on Cunningham et al., (2003) using these cell models. This and more advanced network models will be shown in this demonstration.

NeuroML based network models can be created, reconfigured and visualised using neuroConstruct (http://www.neuroConstruct.org). Moreover, simulations can be automatically generated and run on the above mentioned simulators. The network models can also be generated for execution in parallel computing environments using Parallel NEURON significantly speeding up run time.

Our results show that even in complex networks featuring multiple cell types and thousands of excitatory, inhibitory and electrical synapses, convergence in the behaviour of subcellular variables and spike times between simulators is possible. However, this agreement often only occurs at the limits of spatial and temporal discretisation. This highlights a strong dependence of the exact spike times of a cell model on the method of numerical integration and illustrates the benefit of NeuroML enabled model validation across simulators.

Making highly detailed cell and network models such as this available in a simulator independent language, coupled with simulator and graphical development environment support will allow a wider range of neuroscientists to use, build on and improve these complex models in their investigations.

The work on converting this model to NeuroML has been funded by the Welcome Trust, Medical Research Council and the EU Synapse project. Details on funding for the NeuroML initiative are at http://www.neuroml.org/acknowledgments.

References

1. Gleeson P, Crook S, Cannon R, Hines M, Billings GO, Farinella M, Morse TM, Davison AP, Ray S, Bhalla US, Barnes SR, Dimitrova YD, Silver RA (2010) NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail PLoS Computational Biology, in press

2. Traub RD, Contreras D, Cunningham MO, Murray H, LeBeau FE, Roopun A, Bibbig A, Wilent WB, Higley MJ, Whittington MA (2005) Single-column thalamocortical network model exhibiting gamma oscillations, sleep spindles, and epileptogenic bursts. J Neurophysiol 93: 2194-2232.

3. Cunningham MO, Whittington MA, Bibbig A, Roopun A, LeBeau FE, Vogt A, Monyer H, Buhl EH, Traub RD (2004) A role for fast rhythmic bursting neurons in cortical gamma oscillations in vitro. Proc NatlAcadSci U S A 101: 7152-7157.

Conference: Neuroinformatics 2010 , Kobe, Japan, 30 Aug - 1 Sep, 2010.

Presentation Type: Oral Presentation

Topic: Large scale modeling

Citation: Gleeson P, Farinella M, Billings GO and Silver AR (2010). A thalamocortical network model in NeuroML. Front. Neurosci. Conference Abstract: Neuroinformatics 2010 . doi: 10.3389/conf.fnins.2010.13.00089

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Received: 14 Jun 2010; Published Online: 14 Jun 2010.

* Correspondence: Padraig Gleeson, University College London, Department of Neuroscience, Physiology and Pharmacology, London, United Kingdom, p.gleeson@ucl.ac.uk