title: Spontaneous activity does not predict morphological type in cerebellar interneurons.
abstract: The effort to determine morphological and anatomically defined neuronal characteristics from extracellularly recorded physiological signatures has been attempted with varying success in different brain areas. Recent studies have attempted such classification of cerebellar interneurons (CINs) based on statistical measures of spontaneous activity. Previously, such efforts in different brain areas have used supervised clustering methods based on standard parameterizations of spontaneous interspike interval (ISI) histograms. We worried that this might bias researchers toward positive identification results and decided to take a different approach. We recorded CINs from anesthetized cats. We used unsupervised clustering methods applied to a nonparametric representation of the ISI histograms to identify groups of CINs with similar spontaneous activity and then asked how these groups map onto different cell types. Our approach was a fuzzy C-means clustering algorithm applied to the Kullbach-Leibler distances between ISI histograms. We found that there is, in fact, a natural clustering of the spontaneous activity of CINs into six groups but that there was no relationship between this clustering and the standard morphologically defined cell types. These results proved robust when generalization was tested to completely new datasets, including datasets recorded under different anesthesia conditions and in different laboratories and different species (rats). Our results suggest the importance of an unsupervised approach in categorizing neurons according to their extracellular activity. Indeed, a reexamination of such categorization efforts throughout the brain may be necessary. One important open question is that of functional differences of our six spontaneously defined clusters during actual behavior.
authors: Department of Biomedical Engineering, Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel.
authors: Shlomi
authors: S
authors: Haar
authors: Faculty of Health Science, and Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel.
authors: Ronit
authors: R
authors: Givon-Mayo
authors: Department of Physiology and Pharmacology, Oregon Health and Science University, Portland, Oregon 97239, and.
authors: Neal H
authors: NH
authors: Barmack
authors: Department of Physiology and Pharmacology, Oregon Health and Science University, Portland, Oregon 97239, and.
authors: Vadim
authors: V
authors: Yakhnitsa
authors: Department of Biomedical Engineering, Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel, Department of Neuroscience, Erasmus Medical Center, 3015 CE Rotterdam, The Netherlands donchin@bgu.ac.il.
authors: Opher
authors: http://orcid.org/0000-0003-0963-4467
authors: O
authors: Donchin
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D001710:Biotin / Q000031:analogs & derivatives / Q000378:metabolism; D002415:Cats; D002531:Cerebellum / Q000166:cytology*; D016000:Cluster Analysis; D003198:Computer Simulation; D007395:Interneurons / Q000145:classification* / Q000166:cytology*; D051379:Mice; D008959:Models, Neurological; D051381:Rats; D013045:Species Specificity
chemical_list: C070614:neurobiotin; D001710:Biotin
keywords: cerebellar cortex interneurons; fuzzy C-means clustering; inter-spike interval
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