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New insights into the classification and nomenclature of cortical GABAergic interneurons

2013; Nature Portfolio; Volume: 14; Issue: 3 Linguagem: Inglês

10.1038/nrn3444

1471-0048

Javier DeFelipe, Pedro L. López-Cruz, Ruth Benavides‐Piccione, Concha Bielza, Pedro Larrañaga, Stewart A. Anderson, Andreas Burkhalter, Bruno Cauli, Alfonso Fairén, Dirk Feldmeyer, Gord Fishell, David Fitzpatrick, Tamás F. Freund, Guillermo González‐Burgos, Shaul Hestrin, Sean Hill, Patrick R. Hof, Z. Josh Huang, Edward G. Jones, Yasuo Kawaguchi, Zoltán F. Kisvárday, Yoshiyuki Kubota, David A. Lewis, Óscar Marín, Henry Markram, Chris J. McBain, Hanno S. Meyer, Hannah Monyer, Sacha B. Nelson, Kathleen S. Rockland, Jean Rossier, John L.R. Rubenstein, Bernardo Rudy, Massimo Scanziani, Gordon M. Shepherd, Chet C. Sherwood, Jochen F. Staiger, Gábor Tamás, Alex M. Thomson, Yun Wang, Rafael Yuste, Giorgio A. Ascoli,

Gene Regulatory Network Analysis

The classification of cortical neurons, including interneurons, remains a thorny issue in neuroscience. This Analysis article presents and tests a possible taxonomical solution for classifying cortical GABAergic interneurons based on a web-based interactive system that allows experts to classify neurons with pre-determined morphological criteria. A systematic classification and accepted nomenclature of neuron types is much needed but is currently lacking. This article describes a possible taxonomical solution for classifying GABAergic interneurons of the cerebral cortex based on a novel, web-based interactive system that allows experts to classify neurons with pre-determined criteria. Using Bayesian analysis and clustering algorithms on the resulting data, we investigated the suitability of several anatomical terms and neuron names for cortical GABAergic interneurons. Moreover, we show that supervised classification models could automatically categorize interneurons in agreement with experts' assignments. These results demonstrate a practical and objective approach to the naming, characterization and classification of neurons based on community consensus.