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 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.
authors: Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania; Program in Neural Computation, Carnegie Mellon University, Pittsburgh, Pennsylvania;
authors: Shreejoy J
authors: http://orcid.org/0000-0002-1007-9061
authors: SJ
authors: Tripathy
authors: Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania; Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania;
authors: Shawn D
authors: SD
authors: Burton
authors: Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania; Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania; and.
authors: Matthew
authors: M
authors: Geramita
authors: 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.
authors: Richard C
authors: http://orcid.org/0000-0002-2940-3378
authors: RC
authors: Gerkin
authors: 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.
authors: Nathaniel N
authors: NN
authors: Urban
mesh_terms: D000818:Animals; D000831:Animals, Newborn; D001703:Biophysics; D001921:Brain / Q000166:cytology*; D016000:Cluster Analysis; D066264:Datasets as Topic; D006801:Humans; D066298:In Vitro Techniques; D016014:Linear Models; D008164:Luminescent Proteins / Q000235:genetics / Q000378:metabolism; D008564:Membrane Potentials / Q000502:physiology*; D051379:Mice; D008822:Mice, Transgenic; D008959:Models, Neurological; D009474:Neurons / Q000145:classification* / Q000502:physiology*; D018408:Patch-Clamp Techniques
chemical_list: D008164:Luminescent Proteins
keywords: databases; electrophysiology; intrinsic membrane properties; neuroinformatics; neuron biophysics; neuron diversity; text mining