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Elife. 2015 Apr 30;4:e04250. doi: 10.7554/eLife.04250.

Automatic discovery of cell types and microcircuitry from neural connectomics.

Author information

1
Department of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, United States.
2
Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, United States.

Abstract

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 non-parametric Bayesian technique that identifies neuron types and microcircuitry patterns in connectomics data. It combines the information traditionally used by biologists in a principled and probabilistically coherent manner, including connectivity, cell body location, and the spatial distribution of synapses. 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 Caenorhabditis elegans and an old man-made microprocessor. Our approach extracts structural meaning from connectomics, enabling new approaches of automatically deriving anatomical insights from these emerging datasets.

KEYWORDS:

C. elegans; computation; connectomics; microcircuitry; mouse; neuroscience

PMID:
25928186
PMCID:
PMC4415525
DOI:
10.7554/eLife.04250
[Indexed for MEDLINE]
Free PMC Article

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