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J Neurophysiol. 2015 Jun 1;113(10):3474-89. doi: 10.1152/jn.00237.2015. Epub 2015 Mar 25.

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

Author information

1
Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania; Program in Neural Computation, Carnegie Mellon University, Pittsburgh, Pennsylvania;
2
Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania; Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania;
3
Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania; Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania; and.
4
Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania; Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania; School of Life Sciences, Arizona State University, Tempe, Arizona.
5
Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania; Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania; Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania; and nurban@cmu.edu.

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 literature-based 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 theta-band frequencies.

KEYWORDS:

databases; electrophysiology; intrinsic membrane properties; neuroinformatics; neuron biophysics; neuron diversity; text mining

PMID:
25810482
PMCID:
PMC4455486
DOI:
10.1152/jn.00237.2015
[Indexed for MEDLINE]
Free PMC Article

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