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Published on Oct 16, 2014
New techniques produce massive data about neural connectivity, necessitating new analysis methods to discover the biological and computational basis of this connectivity. It has long been assumed that discovering the local patterns of microcircuitry is crucial to understanding neural function. Here we developed a nonparametric Bayesian technique that identifies neuron types and microcircuitry patterns in connectomics data. We show that the approach recovers known neuron types in the retina, reveals interesting structure in the nervous system of c. elegans, and automatically discovers the structure of microprocessors. Our approach extracts structural meaning from connectomics, enabling new approaches of deriving anatomical insights from these emerging datasets.