The open connectome project data cluster: scalable analysis and vision for high-throughput neuroscience

@article{Burns2013TheOC,
  title={The open connectome project data cluster: scalable analysis and vision for high-throughput neuroscience},
  author={Randal C. Burns and Kunal Lillaney and Daniel R. Berger and Logan Grosenick and Karl Deisseroth and R. Clay Reid and William Gray Roncal and Priya Manavalan and Davi D Bock and Narayanan Kasthuri and Michael M. Kazhdan and Stephen J. Smith and Dean Kleissas and Eric A. Perlman and Kwanghun Chung and Nicholas C. Weiler and Jeff Lichtman and Alexander S. Szalay and Joshua T. Vogelstein and R. Jacob Vogelstein},
  journal={Scientific and statistical database management : International Conference, SSDBM ... : proceedings. International Conference on Scientific and Statistical Database Management},
  year={2013}
}
We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for time-series and multi-channel data as well. The system was designed primarily for workloads that build connectomes---neural connectivity maps of the brain---using the parallel execution of computer vision algorithms on high-performance compute clusters. These services and open-science data sets are publicly available… CONTINUE READING
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