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2 Publications

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    Bock Lab
    10/01/13 | Optimizing the quantity/quality trade-off in connectome inference.
    Priebe CE, Vogelstein J, Bock D
    Communications in Statistics-Theory and Methods. 2013 Oct;42:3455-62. doi: 10.1080/03610926.2011.630768

    We demonstrate a meaningful prospective power analysis for an (admittedly idealized) illustrative connectome inference task. Modeling neurons as vertices and synapses as edges in a simple random graph model, we optimize the trade-off between the number of (putative) edges identified and the accuracy of the edge identification procedure. We conclude that explicit analysis of the quantity/quality trade-off is imperative for optimal neuroscientific experimental design. In particular, identifying edges faster/more cheaply, but with more error, can yield superior inferential performance.

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    Bock Lab
    06/18/13 | The Open Connectome Project Data Cluster: Scalable analysis and vision for high-throughput neuroscience.
    Burns R, Roncal WG, Kleissas D, Lillaney K, Manavalan P, Perlman E, Berger DR, Bock DD, Chung K, Grosenick L, Kasthuri N, Weiler NC, Deisseroth K, Kazhdan M, Lichtman J, Reid RC, Smith SJ, Szalay AS, Vogelstein JT, Vogelstein RJ
    Scientific and Statistical Database Management: International Conference, SSDBM ... : Proceedings. International Conference on Scientific and Statistical Database Management. 2013 Jun 18:. doi: 10.1145/2484838.2484870

    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 at openconnecto.me. The system design inherits much from NoSQL scale-out and data-intensive computing architectures. We distribute data to cluster nodes by partitioning a spatial index. We direct I/O to different systems-reads to parallel disk arrays and writes to solid-state storage-to avoid I/O interference and maximize throughput. All programming interfaces are RESTful Web services, which are simple and stateless, improving scalability and usability. We include a performance evaluation of the production system, highlighting the effec-tiveness of spatial data organization.

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