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.

Climbing Fibers Mediate Vestibular Modulation of Both “Complex” and “Simple Spikes” in Purkinje Cells

“…(2) Climbing fiber afferents can be characterized by recording the iconic action potentials that they evoke in Purkinje cells [54]. Mossy fiber afferents and cerebellar interneurons cannot be conclusively identified using electrophysiological criteria [55]. However, they can be labeled using the technique of juxtacellular stimulation.…”

Visuo-Vestibular Information Processing by Unipolar Brush Cells in the Rabbit Flocculus

“…Haar et al [ 17 ] have questioned the validity of our decision algorithm. They attempted to evaluate it in two ways: (1) by using a simulated data set based on a sample of statistics with which we developed our algorithm [ 12 ] and (2) by applying our algorithm to spontaneous firing statistics from juxtacellularly identified cells collected in the mouse vestibulocerebellum [ 18 ].…”

“…They attempted to evaluate it in two ways: (1) by using a simulated data set based on a sample of statistics with which we developed our algorithm [ 12 ] and (2) by applying our algorithm to spontaneous firing statistics from juxtacellularly identified cells collected in the mouse vestibulocerebellum [ 18 ]. With respect to the first attempt, Haar et al [ 17 ] created simulated data assuming a multivariate normal distribution of the five statistical parameters used in our decision algorithm [ 12 ]. However, the assumption of normality is not supported by our data (see Fig.…”

“…With respect to the second attempted evaluation, juxtacellular data were obtained from compromised cerebella [ 18 ] as evidenced by both the abnormally low average Purkinje cell spontaneous complex spike rate (0.2 Hz, far from the universally recognized average value of about 1 Hz) and the abnormally low average spontaneous simple spike rate (20.1 Hz, far from the normal values in mice of about 50 Hz or more; e.g., [ 19 – 21 ]). Furthermore, it is very likely that Haar et al [ 17 ] did not calculate the statistical measures from sufficiently long periods of spontaneous activity. They reported that, of the 92 interneurons made available to them [ 18 ], they used only those 45 with spontaneous activity recordings lasting longer than 10 s but that lower limit is substantially shorter than the typically recommended 60 s [ 12 , 15 ].…”

Eye Movements during Visuomotor Adaptation Represent Only Part of the Explicit Learning

“…We tested clustering for two through six clusters. Following Haar et al (2015a), we used the cluster validity index proposed by Zhang et al (2008). This index uses a ratio between a variation measure in each cluster and a separation measure be-tween the fuzzy clusters.…”