  Neural connectomics has begun producing massive amounts of data,
necessitating new analysis methods to discover the biological and computational
structure. It has long been assumed that discovering neuron types and their
relation to microcircuitry is crucial to understanding neural function. Here we
developed a nonparametric Bayesian technique that identifies neuron types and
microcircuitry patterns in connectomics data. It combines the information
traditionally used by biologists, including connectivity, cell body location
and the spatial distribution of synapses, in a principled and
probabilistically-coherent manner. We show that the approach recovers known
neuron types in the retina and enables predictions of connectivity, better than
simpler algorithms. It also can reveal interesting structure in the nervous
system of C. elegans, and automatically discovers the structure of a
microprocessor. Our approach extracts structural meaning from connectomics,
enabling new approaches of automatically deriving anatomical insights from
these emerging datasets.
