DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: 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}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.7554/eLife.04250

Citation Metrics:
Cited by: 28 works
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

The fundamental plan of the retina
journal, September 2001


Modality and Topographic Properties of Single Neurons of Cat'S Somatic Sensory Cortex
journal, July 1957


Community structure in social and biological networks
journal, June 2002

  • Girvan, M.; Newman, M. E. J.
  • Proceedings of the National Academy of Sciences, Vol. 99, Issue 12
  • DOI: 10.1073/pnas.122653799

The frontal cortex: does size matter?
journal, March 2002


Structural Properties of the Caenorhabditis elegans Neuronal Network
journal, February 2011


Wiring optimization can relate neuronal structure and function
journal, March 2006

  • Chen, B. L.; Hall, D. H.; Chklovskii, D. B.
  • Proceedings of the National Academy of Sciences, Vol. 103, Issue 12
  • DOI: 10.1073/pnas.0506806103

A Hierarchical Structure of Cortical Interneuron Electrical Diversity Revealed by Automated Statistical Analysis
journal, September 2012

  • Druckmann, Shaul; Hill, Sean; Schürmann, Felix
  • Cerebral Cortex, Vol. 23, Issue 12
  • DOI: 10.1093/cercor/bhs290

Why not connectomics?
journal, May 2013

  • Morgan, Joshua L.; Lichtman, Jeff W.
  • Nature Methods, Vol. 10, Issue 6
  • DOI: 10.1038/nmeth.2480

Statistical connectivity provides a sufficient foundation for specific functional connectivity in neocortical neural microcircuits
journal, September 2012

  • Hill, S. L.; Wang, Y.; Riachi, I.
  • Proceedings of the National Academy of Sciences, Vol. 109, Issue 42
  • DOI: 10.1073/pnas.1202128109

Sequencing the Connectome
journal, October 2012


Variational Bayesian inference for the Latent Position Cluster Model for network data
journal, January 2013

  • Salter-Townshend, Michael; Murphy, Thomas Brendan
  • Computational Statistics & Data Analysis, Vol. 57, Issue 1
  • DOI: 10.1016/j.csda.2012.08.004

Neuronal Cell Types and Connectivity: Lessons from the Retina
journal, September 2014


THE CIRCUITRY OF V1 AND V2: Integration of Color, Form, and Motion
journal, July 2005


A functional microcircuit for cat visual cortex.
journal, August 1991


Collective dynamics of ‘small-world’ networks
journal, June 1998

  • Watts, Duncan J.; Strogatz, Steven H.
  • Nature, Vol. 393, Issue 6684
  • DOI: 10.1038/30918

Estimation and Prediction for Stochastic Blockstructures
journal, September 2001

  • Nowicki, Krzysztof; Snijders, Tom A. B.
  • Journal of the American Statistical Association, Vol. 96, Issue 455
  • DOI: 10.1198/016214501753208735

Slice sampling
journal, June 2003


Cell-type identity: a key to unlocking the function of neocortical circuits
journal, August 2009


Comparing partitions
journal, December 1985

  • Hubert, Lawrence; Arabie, Phipps
  • Journal of Classification, Vol. 2, Issue 1
  • DOI: 10.1007/BF01908075

The Next-Generation Sequencing Revolution and Its Impact on Genomics
journal, September 2013


Visualizing a classic CPU in action: the 6502
conference, January 2010

  • James, Greg; Silverman, Barry; Silverman, Brian
  • ACM SIGGRAPH 2010 Talks on - SIGGRAPH '10
  • DOI: 10.1145/1837026.1837061

Microcircuits in action – from CPGs to neocortex
journal, October 2005


Interneurons of the hippocampus
journal, January 1996


Connectomic reconstruction of the inner plexiform layer in the mouse retina
journal, August 2013

  • Helmstaedter, Moritz; Briggman, Kevin L.; Turaga, Srinivas C.
  • Nature, Vol. 500, Issue 7461
  • DOI: 10.1038/nature12346

Comparison between supervised and unsupervised classifications of neuronal cell types: A case study
journal, December 2010

  • Guerra, Luis; McGarry, Laura M.; Robles, Víctor
  • Developmental Neurobiology, Vol. 71, Issue 1
  • DOI: 10.1002/dneu.20809

The Structure of the Nervous System of the Nematode Caenorhabditis elegans
journal, November 1986

  • White, J. G.; Southgate, E.; Thomson, J. N.
  • Philosophical Transactions of the Royal Society B: Biological Sciences, Vol. 314, Issue 1165
  • DOI: 10.1098/rstb.1986.0056

Characterization of Neocortical Principal Cells and Interneurons by Network Interactions and Extracellular Features
journal, July 2004

  • Barthó, Peter; Hirase, Hajime; Monconduit, Lenaïc
  • Journal of Neurophysiology, Vol. 92, Issue 1
  • DOI: 10.1152/jn.01170.2003

The columnar organization of the neocortex
journal, April 1997


Coding of color and form in the geniculostriate visual pathway (invited review)
journal, January 2005

  • Lennie, Peter; Movshon, J. Anthony
  • Journal of the Optical Society of America A, Vol. 22, Issue 10
  • DOI: 10.1364/JOSAA.22.002013

Network Motifs: Simple Building Blocks of Complex Networks
journal, October 2002