Model validation using the Mozaik framework
One of the current challenges in computational neuroscience is not only to build high fidelity models of brain structures reflecting their biological complexity, but also to test them with as broad a range of measures and stimulation conditions as possible. The need to repetitively perform such tests on a model under development, and the increasing need to make such thorough comparisons between different models, can make manual operation of such testing schemes extremely time consuming and error prone, greatly decreasing productivity. This highlights the increasing need for automation of these processes. However, ad-hoc 'in-house' automation of such processes can itself take a significant amount of time and typically requires further work each time the specifications of the project change. The recently released Mozaik framework [1] formalizes the entire testing workflow using a general API, offering full automation of stimulation, data-collection and subsequent analysis and visualization of spiking neuronal network models, thus addressing both the automation of testing workflows and the generalization of such automation to a wide range of workflow types. Here we present a specific use-case of applying the Mozaik framework to incremental development of a detailed model of the thalamo-cortical loop, which is being continuously tested against a range of measures under several stimulation paradigms.
Acknowledgements
This work was supported by the CNRS and by the European Union through projects FP7-269921 (BrainScaleS) and FP7-604102 (Human Brain Project).
References
[1] Antolík J and Davison AP (2013) Integrated workflows for spiking neuronal network simulations. Front. Neuroinform. 7:34. doi: 10.3389/fninf.2013.00034
Keywords:
Vision,
LGN,
workflow systems,
model organism,
neuronal network model
Conference:
Neuroinformatics 2014, Leiden, Netherlands, 25 Aug - 27 Aug, 2014.
Presentation Type:
Poster, not to be considered for oral presentation
Topic:
Computational neuroscience
Citation:
Guarino
D,
Antolík
J and
Davison
AP
(2014). Model validation using the Mozaik framework.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2014.
doi: 10.3389/conf.fninf.2014.18.00037
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Received:
04 Apr 2014;
Published Online:
04 Jun 2014.
*
Correspondence:
Dr. Andrew P Davison, CNRS, UNIC, Gif sur Yvette, 91198, France, andrew.davison@cnrs.fr