期刊
JOURNAL OF NEUROPHYSIOLOGY
卷 113, 期 10, 页码 3474-3489出版社
AMER PHYSIOLOGICAL SOC
DOI: 10.1152/jn.00237.2015
关键词
neuron biophysics; intrinsic membrane properties; electrophysiology; neuron diversity; neuroinformatics; text mining; databases
资金
- National Science Foundation Graduate Fellowship
- National Institute of Deafness and Other Communications Disorders [F31-DC-013490, F32-DC-010535, R01-DC-005798]
- National Institute of Mental Health [R01-MH-081905]
- Pennsylvania Department of Health's Commonwealth Universal Research Enhancement Program
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.
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