Probabilistic Identification of Cerebellar Cortical Neurones across Species

Gert van Dijck, Marc M. van Hulle, Shane A. Heiney, Pablo M. Blazquez, Hui Meng, Dora Angelaki, Alexander Arenz, Troy W. Margrie, Abteen Mostofi, Steve Edgley, Fredrik Bengtsson, Carl Fredrik Ekerot, Henrik Jörntell, Jeffrey W. Dalley, Tahl Holtzman

Research output: Contribution to journalArticle

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 equi-probable 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.

Original languageEnglish (US)
Article numbere57669
JournalPLoS One
Volume8
Issue number3
DOIs
StatePublished - Mar 4 2013

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Neurons
neurons
Statistics
Classifiers
taxonomy
cells
statistics
Animals
Entropy
Cerebellar Cortex
Purkinje Cells
Equus
Automatic Data Processing
Networks (circuits)
entropy
Haplorhini
Cats
monkeys
cortex
Rabbits

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this

van Dijck, G., van Hulle, M. M., Heiney, S. A., Blazquez, P. M., Meng, H., Angelaki, D., ... Holtzman, T. (2013). Probabilistic Identification of Cerebellar Cortical Neurones across Species. PLoS One, 8(3), [e57669]. https://doi.org/10.1371/journal.pone.0057669

Probabilistic Identification of Cerebellar Cortical Neurones across Species. / van Dijck, Gert; van Hulle, Marc M.; Heiney, Shane A.; Blazquez, Pablo M.; Meng, Hui; Angelaki, Dora; Arenz, Alexander; Margrie, Troy W.; Mostofi, Abteen; Edgley, Steve; Bengtsson, Fredrik; Ekerot, Carl Fredrik; Jörntell, Henrik; Dalley, Jeffrey W.; Holtzman, Tahl.

In: PLoS One, Vol. 8, No. 3, e57669, 04.03.2013.

Research output: Contribution to journalArticle

van Dijck, G, van Hulle, MM, Heiney, SA, Blazquez, PM, Meng, H, Angelaki, D, Arenz, A, Margrie, TW, Mostofi, A, Edgley, S, Bengtsson, F, Ekerot, CF, Jörntell, H, Dalley, JW & Holtzman, T 2013, 'Probabilistic Identification of Cerebellar Cortical Neurones across Species', PLoS One, vol. 8, no. 3, e57669. https://doi.org/10.1371/journal.pone.0057669
van Dijck G, van Hulle MM, Heiney SA, Blazquez PM, Meng H, Angelaki D et al. Probabilistic Identification of Cerebellar Cortical Neurones across Species. PLoS One. 2013 Mar 4;8(3). e57669. https://doi.org/10.1371/journal.pone.0057669
van Dijck, Gert ; van Hulle, Marc M. ; Heiney, Shane A. ; Blazquez, Pablo M. ; Meng, Hui ; Angelaki, Dora ; Arenz, Alexander ; Margrie, Troy W. ; Mostofi, Abteen ; Edgley, Steve ; Bengtsson, Fredrik ; Ekerot, Carl Fredrik ; Jörntell, Henrik ; Dalley, Jeffrey W. ; Holtzman, Tahl. / Probabilistic Identification of Cerebellar Cortical Neurones across Species. In: PLoS One. 2013 ; Vol. 8, No. 3.
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