Spontaneous activity does not predict morphological type in cerebellar interneurons
Author(s)
Haar, Shlomi
Givon-Mayo, Ronit
Barmack, Neal H
Yakhnitsa, Vadim
Donchin, Opher
Type
Journal Article
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
worriedthatthis might bias researcherstoward positive identification results and decidedtotake 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 ofthe spontaneous activity of CINs into six groups butthatthere was no relationship betweenthis 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.
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
worriedthatthis might bias researcherstoward positive identification results and decidedtotake 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 ofthe spontaneous activity of CINs into six groups butthatthere was no relationship betweenthis 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.
Date Issued
2015-01-28
Date Acceptance
2014-09-23
Citation
The Journal of Neuroscience, 2015, 35 (4), pp.1432-1442
ISSN
0270-6474
Publisher
Society for Neuroscience
Start Page
1432
End Page
1442
Journal / Book Title
The Journal of Neuroscience
Volume
35
Issue
4
Copyright Statement
Copyright © 2015 the authors. For articles published after 2014, the Society for Neuroscience (SfN) retains an exclusive license to publish the article for 6 months; after 6 months, the work becomes available to the public to copy, distribute, or display under the terms of the Creative Commons Attribution 4.0 International License (CC-BY). This license allows data and text mining, use of figures in presentations, and posting the article online, provided that the original article is credited.
License URL
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000349669600011&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Subjects
cerebellar cortex interneurons
DISTANCE MEASURES
FIBERS
fuzzy C-means clustering
GOLGI CELLS
INFORMATION
inter-spike interval
Life Sciences & Biomedicine
MOLECULAR LAYER INTERNEURONS
MOUSE CEREBELLUM
NEURONS
Neurosciences
Neurosciences & Neurology
PROBABILITY
RAT
RESPONSES
Science & Technology
Publication Status
Published
Date Publish Online
2015-01-28