CircuitML: a Modular Language for Modeling Local Processing Units in the Drosophila Brain
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1
Pontifical Catholic University of Rio de Janeiro, Departament of Electrical engineering, Brazil
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2
Columbia University, Departament of Electrical engineering, United States
The brain of the fruit fly Drosophila melanogaster is an attractive model system for studying the logic of neural circuit function because it implements complex sensory-driven behaviors with a nervous system comprising a number of neural components that is five orders of magnitude smaller than that of vertebrates. Analysis of the fly’s connectome, or neural connectivity map, using the extensive toolbox of genetic manipulation techniques developed for Drosophila has revealed that its brain comprises about 40 distinct modular subdivisions called local processing units (LPUs) [1], each of which is characterized by unique internal information processing circuitry. LPUs can be regarded as the functional building blocks of the fly brain because almost all identified LPUs have been found to correspond to anatomical regions of the fly brain associated with specific functional subsystems such as sensation and locomotion. We can therefore cast the task of emulating the entire fly brain as requiring the accurate modeling and integration of its constituent LPUs [1].
Although our knowledge of the internal circuitry of many LPUs is far from complete, analysis of those LPUs comprised by the fly’s olfactory and vision systems suggests the existence of repeated canonical subcircuits [2] that are integral to the information processing functions provided by each LPU. The development of plausible LPU models therefore requires the ability to specify and instantiate subcircuits without explicit reference to their constituent neurons and internal connections. To this end, we have devised a neural circuit specification language called CircuitML for construction of LPUs. CircuitML has been designed as an extension to NeuroML [3] and LEMS [4], XML-based neuronal model description languages for data-driven specification of neural circuit models; it provides constructs for defining subcircuits that comprise neural primitives supported by NeuroML and LEMS. Subcircuits are endowed with interface ports that enable their connection to other subcircuits via neural connectivity patterns. We have used CircuitML to specify an LPU-based model of the fly olfactory system [2] that we simulated using a GPU-based CircuitML processor.
References
[1] Lev E. Givon and Aurel A. Lazar. Neurokernel: An open scalable software framework for the emulation and verification of
Drosophila brain models on multiple GPUs. 2013, submitted for publication.
[2] Aurel A. Lazar, Wenze Li, Nikul H. Ukani, Chung-Heng Yeh, and Yiyin Zhou. Neural circuit abstractions in the fruit fly brain.
2013, submitted for publication.
[3] Padraig Gleeson, Sharon Crook, Robert C. Cannon, Michael L. Hines, Guy O. Billings, Matteo Farinella, Thomas M. Morse,
Andrew P. Davison, Subhasis Ray, Upinder S. Bhalla, Simon R. Barnes, Yoana D. Dimitrova, R. Angus Silver. NeuroML: a
language for describing data driven models of neurons and networks with a high degree of biological detail. PLoS
Computational Biology, 6(6):e1000815, 2010.
[4] Robert C. Cannon, Padraig Gleeson, Sharon Crook, and R. Angus Silver. A declarative model specification system allowing
NeuroML to be extended with user-defined component types. BMC Neuroscience, 13(Suppl 1):P42, 2012.
Keywords:
Drosophila melanogaster,
simulation models,
XML,
python language,
CUDA
Conference:
Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013.
Presentation Type:
Demo
Topic:
Computational neuroscience
Citation:
Salles Chevitarese
D,
Givon
L,
Lazar
AA and
Vellasco
M
(2013). CircuitML: a Modular Language for Modeling Local Processing Units in the Drosophila Brain.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2013.
doi: 10.3389/conf.fninf.2013.09.00011
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Received:
07 Apr 2013;
Published Online:
11 Jul 2013.
*
Correspondence:
Mr. Daniel Salles Chevitarese, Pontifical Catholic University of Rio de Janeiro, Departament of Electrical engineering, Rio de Janeiro, Rio de Janeiro, 38097, Brazil, daniel@chevitarese.com.br