Automatic discovery of cell types and microcircuitry from neural connectomics
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Apr 30, 2015
- Source ID
- 10.7554/elife.04250
Entities
People
- Eric Jonas
- Konrad Körding
Organizations
- Defense Advanced Research Projects Agency
- Lawrence Berkeley National Laboratory
- National Institutes of Health
- National Science Foundation
- Northwestern University
- Shirley Ryan AbilityLab
- University of California, Berkeley