New insights into the classification and nomenclature of cortical GABAergic interneurons
A feature-based classification and agreed-upon nomenclature of GABAergic interneurons of the cerebral cortex is much needed but currently lacking.We designed a web-based interactive system that allowed 42 neuroscience experts to classify a representative sample of 320 cortical neurons and a selected...
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| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
6 February 2013
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| In: |
Nature reviews. Neuroscience
Year: 2013, Jahrgang: 14, Heft: 3, Pages: 202-216 |
| ISSN: | 1471-0048 |
| DOI: | 10.1038/nrn3444 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1038/nrn3444 Verlag, lizenzpflichtig, Volltext: https://www.nature.com/articles/nrn3444 
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| Verfasserangaben: | Javier DeFelipe, Pedro L. López-Cruz, Ruth Benavides-Piccione, Concha Bielza, Pedro Larrañaga, Stewart 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, Josh Huang, Edward G. Jones, Yasuo Kawaguchi, Zoltán Kisvárday, Yoshiyuki Kubota, David A. Lewis, Oscar Marín, Henry Markram, Chris J. McBain, Hanno S. Meyer, Hannah Monyer, Sacha B. Nelson, Kathleen Rockland, Jean Rossier, John L. R. Rubenstein, Bernardo Rudy, Massimo Scanziani, Gordon M. Shepherd, Chet C. Sherwood, Jochen F. Staiger, Gábor Tamás, Alex Thomson, Yun Wang, Rafael Yuste and Giorgio A. Ascoli |
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| 520 | |a A feature-based classification and agreed-upon nomenclature of GABAergic interneurons of the cerebral cortex is much needed but currently lacking.We designed a web-based interactive system that allowed 42 neuroscience experts to classify a representative sample of 320 cortical neurons and a selected set of simple morphology features based on reconstructions of their axonal arbors.The consensus on and usefulness of these features and neuron names were investigated using agreement analysis, clustering algorithms, Bayesian networks and supervised classification on the resulting data.The results quantitatively confirm the impression that different investigators use their own, mutually inconsistent classification schemes based on morphological criteria. However, the analyses also demonstrate that the community may be reaching consensus for a practical approach to the naming of certain anatomical terms that are useful for neuronal characterization and classification.State-of-the-art machine learning approaches were shown to achieve discrimination capability equivalent to or better than human performance, opening the possibility of creating an objective computer tool for automatic classification of neurons, a Neuroclassifier. | ||
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