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.
N1 - Publisher Copyright:
© 2015 the American Physiological Society.
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
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U2 - 10.1152/jn.00237.2015
DO - 10.1152/jn.00237.2015
M3 - Article
C2 - 25810482
AN - SCOPUS:84930845523
SN - 0022-3077
VL - 113
SP - 3474
EP - 3489
JO - Journal of neurophysiology
JF - Journal of neurophysiology
IS - 10
ER -