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Title: Automatic discovery of cell types and microcircuitry from neural connectomics

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.
Authors:
;
Publication Date:
Grant/Contract Number:
Award 7076018
Type:
Published Article
Journal Name:
eLife
Additional Journal Information:
Journal Name: eLife Journal Volume: 4; Journal ID: ISSN 2050-084X
Publisher:
eLife Sciences Publications, Ltd.
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
OSTI Identifier:
1180015
Alternate Identifier(s):
OSTI ID: 1197919

Jonas, Eric, and Kording, Konrad. Automatic discovery of cell types and microcircuitry from neural connectomics. United States: N. p., Web. doi:10.7554/eLife.04250.
Jonas, Eric, & Kording, Konrad. Automatic discovery of cell types and microcircuitry from neural connectomics. United States. doi:10.7554/eLife.04250.
Jonas, Eric, and Kording, Konrad. 2015. "Automatic discovery of cell types and microcircuitry from neural connectomics". United States. doi:10.7554/eLife.04250.
@article{osti_1180015,
title = {Automatic discovery of cell types and microcircuitry from neural connectomics},
author = {Jonas, Eric and Kording, Konrad},
abstractNote = {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.},
doi = {10.7554/eLife.04250},
journal = {eLife},
number = ,
volume = 4,
place = {United States},
year = {2015},
month = {4}
}