Increasing availability of comprehensive experimental datasets in neuroscience and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational network models. 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. We provide reference Application Programming Interfaces and model examples to catalyze support and adoption of the format. 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.
Dai, Kael and Hernando, Juan and Billeh, Yazan N. and Gratiy, Sergey L. and Planas, Judit and Davison, Andrew and Dura-Bernal, Salvador and Gleeson, Padraig and Devresse, Adrien and Gevaert, Michael and Van Geit, Werner A. H. and Povolotsky, Arseny V. and Muller, Eilif and Courcol, Jean-Denis and Arkhipov, Anton, The SONATA Data Format for Efficient Description of Large-Scale Network Models (May 14, 2019). Available at SSRN: https://ssrn.com/abstract=3387685 or http://dx.doi.org/10.2139/ssrn.3387685
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