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 journalArticlepeer-review

49 Scopus citations


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
Issue number10
StatePublished - Jun 1 2015


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

ASJC Scopus subject areas

  • General Neuroscience
  • Physiology


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