The SONATA Data Format for Efficient Description of Large-Scale Network Models

Dai, K.; Hernando, J.; Billeh, Y. N.; Gratiy, S. L.; Planas, J.; Davison, A. P.; Dura-Bernal, S.; Gleeson, P.; Devresse, A.; Gevaert, M.; King, J. G.; Van Geit, W. A. H.; Povolotsky, A. V.; Muller, E.; Courcol, J.-D.; Arkhipov, A.

doi:10.1101/625491

bioRxiv · 10.1101/625491

The SONATA Data Format for Efficient Description of Large-Scale Network Models

Dai, K.·Hernando, J.·Billeh, Y. N.·Gratiy, S. L.·Planas, J.·Davison, A. P.·Dura-Bernal, S.·Gleeson, P.·Devresse, A.·Gevaert, M.·King, J. G.·Van Geit, W. A. H.·Povolotsky, A. V.·Muller, E.·Courcol, J.-D.·Arkhipov, A.

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Abstract

Increasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and simulation, as well as sharing of such large-scale models, a broadly applicable, flexible, and high-performance data format is necessary. To address this need, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. It is designed for memory and computational efficiency and works across multiple platforms. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is used in multiple modeling and visualization tools, and we also provide reference Application Programming Interfaces and model examples to catalyze further adoption. SONATA format is free and open for the community to use and build upon with the goal of enabling efficient model building, sharing, and reproducibility.

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Dai, K., Hernando, J., Billeh, Y. N., Gratiy, S. L., Planas, J., Davison, A. P., Dura-Bernal, S., Gleeson, P., Devresse, A., Gevaert, M., King, J. G., Van Geit, W. A. H., Povolotsky, A. V., Muller, E., Courcol, J.-D., Arkhipov, A.. 2019-05-02. The SONATA Data Format for Efficient Description of Large-Scale Network Models. https://doi.org/10.1101/625491

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