Brain-wide analysis of electrophysiological diversity yields novel categorization of mammalian neuron types

Shreejoy J. Tripathy, Shawn D. Burton, Matthew Geramita, Richard Gerkin, Nathaniel N. Urban

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

For decades, neurophysiologists have characterized the biophysical properties of a rich diversity of neuron types. However, identifying common features and computational roles shared across neuron types is made more difficult by inconsistent conventions for collecting and reporting biophysical data. Here, we leverage NeuroElectro, a literaturebased database of electrophysiological properties (www.neuroelectro. org), to better understand neuronal diversity, both within and across neuron types, and the confounding influences of methodological variability. We show that experimental conditions (e.g., electrode types, recording temperatures, or animal age) can explain a substantial degree of the literature-reported biophysical variability observed within a neuron type. Critically, accounting for experimental metadata enables massive cross-study data normalization and reveals that electrophysiological data are far more reproducible across laboratories than previously appreciated. Using this normalized dataset, we find that neuron types throughout the brain cluster by biophysical properties into six to nine superclasses. These classes include intuitive clusters, such as fast-spiking basket cells, as well as previously unrecognized clusters, including a novel class of cortical and olfactory bulb interneurons that exhibit persistent activity at thetaband frequencies.

Original languageEnglish (US)
Pages (from-to)3474-3489
Number of pages16
JournalJournal of Neurophysiology
Volume113
Issue number10
DOIs
StatePublished - Jun 1 2015

Fingerprint

Neurons
Brain
Olfactory Bulb
Interneurons
Electrodes
Research Design
Databases
Temperature

Keywords

  • Databases
  • Electrophysiology
  • Intrinsic membrane properties
  • Neuroinformatics
  • Neuron biophysics
  • Neuron diversity
  • Text mining

ASJC Scopus subject areas

  • Neuroscience(all)
  • Physiology

Cite this

Brain-wide analysis of electrophysiological diversity yields novel categorization of mammalian neuron types. / Tripathy, Shreejoy J.; Burton, Shawn D.; Geramita, Matthew; Gerkin, Richard; Urban, Nathaniel N.

In: Journal of Neurophysiology, Vol. 113, No. 10, 01.06.2015, p. 3474-3489.

Research output: Contribution to journalArticle

Tripathy, Shreejoy J. ; Burton, Shawn D. ; Geramita, Matthew ; Gerkin, Richard ; Urban, Nathaniel N. / Brain-wide analysis of electrophysiological diversity yields novel categorization of mammalian neuron types. In: Journal of Neurophysiology. 2015 ; Vol. 113, No. 10. pp. 3474-3489.
@article{8e9d90951ace4609b38f8a5b4e12e96c,
title = "Brain-wide analysis of electrophysiological diversity yields novel categorization of mammalian neuron types",
abstract = "For decades, neurophysiologists have characterized the biophysical properties of a rich diversity of neuron types. However, identifying common features and computational roles shared across neuron types is made more difficult by inconsistent conventions for collecting and reporting biophysical data. Here, we leverage NeuroElectro, a literaturebased database of electrophysiological properties (www.neuroelectro. org), to better understand neuronal diversity, both within and across neuron types, and the confounding influences of methodological variability. We show that experimental conditions (e.g., electrode types, recording temperatures, or animal age) can explain a substantial degree of the literature-reported biophysical variability observed within a neuron type. Critically, accounting for experimental metadata enables massive cross-study data normalization and reveals that electrophysiological data are far more reproducible across laboratories than previously appreciated. Using this normalized dataset, we find that neuron types throughout the brain cluster by biophysical properties into six to nine superclasses. These classes include intuitive clusters, such as fast-spiking basket cells, as well as previously unrecognized clusters, including a novel class of cortical and olfactory bulb interneurons that exhibit persistent activity at thetaband frequencies.",
keywords = "Databases, Electrophysiology, Intrinsic membrane properties, Neuroinformatics, Neuron biophysics, Neuron diversity, Text mining",
author = "Tripathy, {Shreejoy J.} and Burton, {Shawn D.} and Matthew Geramita and Richard Gerkin and Urban, {Nathaniel N.}",
year = "2015",
month = "6",
day = "1",
doi = "10.1152/jn.00237.2015",
language = "English (US)",
volume = "113",
pages = "3474--3489",
journal = "Journal of Neurophysiology",
issn = "0022-3077",
publisher = "American Physiological Society",
number = "10",

}

TY - JOUR

T1 - Brain-wide analysis of electrophysiological diversity yields novel categorization of mammalian neuron types

AU - Tripathy, Shreejoy J.

AU - Burton, Shawn D.

AU - Geramita, Matthew

AU - Gerkin, Richard

AU - Urban, Nathaniel N.

PY - 2015/6/1

Y1 - 2015/6/1

N2 - For decades, neurophysiologists have characterized the biophysical properties of a rich diversity of neuron types. However, identifying common features and computational roles shared across neuron types is made more difficult by inconsistent conventions for collecting and reporting biophysical data. Here, we leverage NeuroElectro, a literaturebased database of electrophysiological properties (www.neuroelectro. org), to better understand neuronal diversity, both within and across neuron types, and the confounding influences of methodological variability. We show that experimental conditions (e.g., electrode types, recording temperatures, or animal age) can explain a substantial degree of the literature-reported biophysical variability observed within a neuron type. Critically, accounting for experimental metadata enables massive cross-study data normalization and reveals that electrophysiological data are far more reproducible across laboratories than previously appreciated. Using this normalized dataset, we find that neuron types throughout the brain cluster by biophysical properties into six to nine superclasses. These classes include intuitive clusters, such as fast-spiking basket cells, as well as previously unrecognized clusters, including a novel class of cortical and olfactory bulb interneurons that exhibit persistent activity at thetaband frequencies.

AB - For decades, neurophysiologists have characterized the biophysical properties of a rich diversity of neuron types. However, identifying common features and computational roles shared across neuron types is made more difficult by inconsistent conventions for collecting and reporting biophysical data. Here, we leverage NeuroElectro, a literaturebased database of electrophysiological properties (www.neuroelectro. org), to better understand neuronal diversity, both within and across neuron types, and the confounding influences of methodological variability. We show that experimental conditions (e.g., electrode types, recording temperatures, or animal age) can explain a substantial degree of the literature-reported biophysical variability observed within a neuron type. Critically, accounting for experimental metadata enables massive cross-study data normalization and reveals that electrophysiological data are far more reproducible across laboratories than previously appreciated. Using this normalized dataset, we find that neuron types throughout the brain cluster by biophysical properties into six to nine superclasses. These classes include intuitive clusters, such as fast-spiking basket cells, as well as previously unrecognized clusters, including a novel class of cortical and olfactory bulb interneurons that exhibit persistent activity at thetaband frequencies.

KW - Databases

KW - Electrophysiology

KW - Intrinsic membrane properties

KW - Neuroinformatics

KW - Neuron biophysics

KW - Neuron diversity

KW - Text mining

UR - http://www.scopus.com/inward/record.url?scp=84930845523&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84930845523&partnerID=8YFLogxK

U2 - 10.1152/jn.00237.2015

DO - 10.1152/jn.00237.2015

M3 - Article

C2 - 25810482

AN - SCOPUS:84930845523

VL - 113

SP - 3474

EP - 3489

JO - Journal of Neurophysiology

JF - Journal of Neurophysiology

SN - 0022-3077

IS - 10

ER -