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.
- Authors:
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1180015
- Alternate Identifier(s):
- OSTI ID: 1197919
- Grant/Contract Number:
- Award 7076018
- Resource 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.
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Jonas, Eric, and Kording, Konrad. Automatic discovery of cell types and microcircuitry from neural connectomics. United States: N. p., 2015.
Web. doi:10.7554/eLife.04250.
Jonas, Eric, & Kording, Konrad. Automatic discovery of cell types and microcircuitry from neural connectomics. United States. https://doi.org/10.7554/eLife.04250
Jonas, Eric, and Kording, Konrad. Thu .
"Automatic discovery of cell types and microcircuitry from neural connectomics". United States. https://doi.org/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 = {Thu Apr 30 00:00:00 EDT 2015},
month = {Thu Apr 30 00:00:00 EDT 2015}
}
https://doi.org/10.7554/eLife.04250
Web of Science
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