Journal article
Probabilistic Identification of Cerebellar Cortical Neurones across Species
PloS one, Vol.8(3), pp.e57669-e57669
03/04/2013
DOI: 10.1371/journal.pone.0057669
PMCID: PMC3587648
PMID: 23469215
Abstract
Despite our fine-grain anatomical knowledge of the cerebellar cortex, electrophysiological studies of circuit information processing over the last fifty years have been hampered by the difficulty of reliably assigning signals to identified cell types. We approached this problem by assessing the spontaneous activity signatures of identified cerebellar cortical neurones. A range of statistics describing firing frequency and irregularity were then used, individually and in combination, to build Gaussian Process Classifiers (GPC) leading to a probabilistic classification of each neurone type and the computation of equiprobable decision boundaries between cell classes. Firing frequency statistics were useful for separating Purkinje cells from granular layer units, whilst firing irregularity measures proved most useful for distinguishing cells within granular layer cell classes. Considered as single statistics, we achieved classification accuracies of 72.5% and 92.7% for granular layer and molecular layer units respectively. Combining statistics to form twin-variate GPC models substantially improved classification accuracies with the combination of mean spike frequency and log-interval entropy offering classification accuracies of 92.7% and 99.2% for our molecular and granular layer models, respectively. A cross-species comparison was performed, using data drawn from anaesthetised mice and decerebrate cats, where our models offered 80% and 100% classification accuracy. We then used our models to assess non-identified data from awake monkeys and rabbits in order to highlight subsets of neurones with the greatest degree of similarity to identified cell classes. In this way, our GPC-based approach for tentatively identifying neurones from their spontaneous activity signatures, in the absence of an established ground-truth, nonetheless affords the experimenter a statistically robust means of grouping cells with properties matching known cell classes. Our approach therefore may have broad application to a variety of future cerebellar cortical investigations, particularly in awake animals where opportunities for definitive cell identification are limited.
Details
- Title: Subtitle
- Probabilistic Identification of Cerebellar Cortical Neurones across Species
- Creators
- Gert Van Dijck - University of CambridgeMarc M. Van Hulle - KU LeuvenShane A. Heiney - Washington University in St. LouisPablo M. Blazquez - Washington University in St. LouisHui Meng - Washington Univ, Sch Med, Dept Neurobiol, St Louis, MO USADora E. Angelaki - Washington University in St. LouisAlexander Arenz - Natl Inst Med Res, Div Neurophysiol, London NW7 1AA, EnglandTroy W. Margrie - University College LondonAbteen Mostofi - University of CambridgeSteve Edgley - University of CambridgeFredrik Bengtsson - Lund UniversityCarl-Fredrik Ekerot - Lund UniversityHenrik Jorntell - Lund UniversityJeffrey W. Dalley - University of CambridgeTahl Holtzman - University of Cambridge
- Resource Type
- Journal article
- Publication Details
- PloS one, Vol.8(3), pp.e57669-e57669
- Publisher
- Public Library Science
- DOI
- 10.1371/journal.pone.0057669
- PMID
- 23469215
- PMCID
- PMC3587648
- ISSN
- 1932-6203
- eISSN
- 1932-6203
- Number of pages
- 13
- Grant note
- Isaac Newton Trust Wellcome Trust School of Biological Sciences, Cambridge University; UK Research & Innovation (UKRI); Biotechnology and Biological Sciences Research Council (BBSRC) GOA 10/019 / Flemish Regional Ministry of Education (Belgium); FWO Medical Research Council, UK; UK Research & Innovation (UKRI); Medical Research Council UK (MRC) Trinity College, Cambridge PFV/10/008 / Financing program of the K. U. Leuven Flemish Agency for Innovation by Science and Technology IUAP P7/21 / Interuniversity Attraction Poles Programme; Belgian Federal Science Policy Office BBS/B/16984 / BBSRC; UK Research & Innovation (UKRI); Biotechnology and Biological Sciences Research Council (BBSRC) BBS/B/16984 / Biotechnology and Biological Sciences Research Council; UK Research & Innovation (UKRI); Biotechnology and Biological Sciences Research Council (BBSRC) Michael and Morven Heller Research Fellowship in Computing Science at St Catharine's College, Cambridge G1000183B; G0001354; G0001354B / Medical Research Council; UK Research & Innovation (UKRI); Medical Research Council UK (MRC) G.0588.09 / Belgian Fund for Scientific Research - Flanders; FWO MC_U1175975156 / Medical Research Council, UK; UK Research & Innovation (UKRI); Medical Research Council UK (MRC) CREA/07/027 / CREA of the K. U. Leuven Belgian Fund for Scientific Research - Flanders (FWO); FWO
- Language
- English
- Date published
- 03/04/2013
- Academic Unit
- Iowa Neuroscience Institute; Neuroscience and Pharmacology
- Record Identifier
- 9984622052102771
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