Volume 13 Supplement 1

Twenty First Annual Computational Neuroscience Meeting: CNS*2012

Open Access

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

  • Armando L Rodriguez1Email author,
  • Hugo Cornelis2,
  • David Beeman3 and
  • James M Bower1
BMC Neuroscience201213(Suppl 1):P176

DOI: 10.1186/1471-2202-13-S1-P176

Published: 16 July 2012

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).

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.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2202-13-S1-P176/MediaObjects/12868_2012_Article_2713_Fig1_HTML.jpg
Figure 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.

Declarations

Acknowledgements

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

Authors’ Affiliations

(1)
Barshop Institute, University of Texas Health Science Center
(2)
Department of Neurophysiology, Catholic University of Leuven
(3)
Department of Electrical, Computer, and Energy Engineering, University of Colorado

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-doi:10.1371/journal.pone.0028956View ArticleGoogle Scholar
  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.0029018View ArticleGoogle Scholar

Copyright

© Rodriguez et al; licensee BioMed Central Ltd. 2012

This article is published under license to 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.

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