Synaptic plasticity and pattern recognition in cerebellar Purkinje cells

Giseli De Sousa, Reinoud Maex, Roderick Adams, N. Davey, Volker Steuber

Research output: Chapter in Book/Report/Conference proceedingChapter


Many theories of cerebellar learning assume that long-term depression (LTD) of synapses between parallel fibres (PFs) and Purkinje cells is the basis for pattern recognition in the cerebellum. Here we describe a series of computer simulations that use a morphologically realistic conductance-based model of a cerebellar Purkinje cell to study pattern recognition based on PF LTD. Our simulation results, which are supported by electrophysiological recordings in vitro and in vivo, suggest that Purkinje cells can use a novel neural code that is based on the duration of silent periods in their activity. The simulations of the biologically detailed Purkinje cell model are compared with simulations of a corresponding artificial neural network (ANN) model. We find that the predictions of the two models differ to a large extent. The Purkinje cell model is very sensitive to the amount of LTD induced, whereas the ANN is not. Moreover, the pattern recognition performance of the ANN increases as the patterns become sparser, while the Purkinje cell model is unable to recognise very sparse patterns. These results highlight that it is important to choose a model at a level of biological detail that fits the research question that is being addressed
Original languageEnglish
Title of host publicationThe Computing Dendrite
EditorsBen Torben-Nielsen, Michiel Remme, Hermann Cuntz
PublisherSpringer Nature
ISBN (Electronic)978-1-4614-8094-5
ISBN (Print)978-1-4614-8093-8
Publication statusPublished - 2014

Publication series

NameSpringer Series in Computational Neuroscience


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