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This article is part of the supplement: Twenty First Annual Computational Neuroscience Meeting: CNS*2012

Open Access Poster presentation

Multiscale modeling with GENESIS 3, using the G-shell and Python

Armando L Rodriguez1*, Hugo Cornelis2, David Beeman3 and James M Bower1

Author Affiliations

1 Barshop Institute , University of Texas Health Science Center, San Antonio, TX 78229, USA

2 Department of Neurophysiology, Catholic University of Leuven, Leuven, 3000, Belgium

3 Department of Electrical, Computer, and Energy Engineering, University of Colorado, Boulder, CO 80309, USA

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BMC Neuroscience 2012, 13(Suppl 1):P176  doi:10.1186/1471-2202-13-S1-P176

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2202/13/S1/P176


Published:16 July 2012

© 2012 Rodriguez et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Poster presentation

The CBI architecture [1] being used as the basis of GENESIS 3 (G-3) allows a single model-container to be used to describe a model spanning many levels of scale. This feature allows a user to transparently run multi-scale simulations. As will be described in further detail during the workshop “Multi-Scale Modeling in Computational Neuroscience II: Challenges and Opportunities", the CBI architecture contains a communication component to upscale and downscale numerical variables when moving across different levels of scale. These new capabilities and advances in G-3 usability also allow interfacing with many Python graphical tools (e.g. wxPython, matplotlib), potential web interfaces (e.g. Django), and other independent modules (e.g. Chemesis-3) for use in simulations that cover multiple levels of scale. Progress in developing Python interfaces to G-3 [2], combined with recent implementation of network and biochemical modeling capabilities in G-3 have allowed us to construct a new series of self-guided hands-on modeling tutorials. These are being introduced at the Introduction to Genesis 3 Workshop held in Luebeck, Germany 30 April – 5 May 2012 (https://www.gradschool.uni-luebeck.de/index.php?id=366 webcite).

This poster provides an introduction to these new modeling capabilities, and to the new instructional material. Additions to the existing G-3 tutorials on use of the G-shell cover network creation commands and the use of the Chemesis-3 module. The rewritten version of the tutorial "Creating large networks with GENESIS" demonstrates the use of Python scripting to create cortical network models in G-3. The tutorial "Adding a GUI to G-3 simulations" shows users how to leverage the Python programming interface to construct visual tools.

As an example, Figure 1 illustrates the use of the new G-3 Netview visualization application to display and replay an animation of the spreading excitation in the RSnet2 simulation that is the basis of the GENESIS network modeling tutorial.

thumbnailFigure 1. A short injection pulse applied to a 32 x 32 cell network of coupled excitatory cells starts a wave of excitation that can be replayed and examined in detail during any time window, using the G-3 Network Viewer.

Acknowledgements

Armando L. Rodriguez and David Beeman are partially supported by NIH grant 3 R01 NS049288-06S1 to James M. Bower.

References

  1. Cornelis H, Coop AD, Bower JM: A Federated Design for a Neurobiological Simulation Engine: The CBI Federated Software Architecture.

    PLoS ONE 2005, 7(1):e28956.

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  2. Cornelis H, Rodriguez AL, Coop AD, Bower JM: Python as a Federation Tool for GENESIS 3.0.

    PLoS ONE 2005, 7(1):e29018.

    doi:10.1371/journal.pone.0029018

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