Abstract
Many cerebellar learning theories assume that long-term depression (LTD) of synapses between parallel fibres (PFs) and Purkinje cells (PCs) provides the basis for pattern recognition in the cerebellum. Previous work has suggested that PCs can use a novel neural code based on the duration of silent periods. These simulations have used a simplified learning rule, where the synaptic conductance was halved each time a pattern was learned. However, experimental studies in cerebellar slices show that the synaptic conductance saturates and is rarely reduced to less than 50% of its baseline value. Moreover, the previous simulations did not include plasticity of the synapses between inhibitory interneurons and PCs. Here we study the effect of LTD saturation and inhibitory synaptic plasticity on pattern recognition in a complex PC model. We find that the PC model is very sensitive to the value at which LTD saturates, but is unaffected by inhibitory synaptic plasticity.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ito, M.: Cerebellar long-term depression: Characterization, signal transduction, and functional roles. Physiol. Rev. 81(3), 1143–1195 (2001)
Marr, D.: A theory of cerebellar cortex. Journal of Physiology (London) 202, 437–470 (1969)
Ito, M.: The cerebellum and neural control. Raven Press, New York (1984)
Steuber, V., Mittmann, W., Hoebeek, F.E., Silver, R.A., De Zeeuw, C.I., Häusser, M., De Schutter, E.: Cerebellar LTD and pattern recognition by purkinje cells. Neuron 54(1), 121–136 (2007)
Mittmann, W., Häusser, M.: Linking synaptic plasticity and spike output at excitatory and inhibitory synapses onto cerebellar purkinje cells. J. Neurosci. 27(21), 5559–5570 (2007)
Wang, S.S.H., Denk, W., Häusser, M.: Coincidence detection in single dendritic spines mediated by calcium release. Nat. Neurosci. 3(12), 1266–1273 (2000)
Bower, J., Beeman, D.: The book of GENESIS: Exploring realistic neural models with the general neural simulation system (2003)
De Schutter, E., Bower, J.: An active membrane model of the cerebellar purkinje cell. I. simulation of current clamps in slice. Journal of Neurophysiology 71(1), 375–400 (1994)
De Schutter, E., Bower, J.: An active membrane model of the cerebellar purkinje cell: II. simulation of synaptic responses. Journal of Neurophysiology 71(1), 401–419 (1994)
Rapp, M., Segev, I., Yarom, Y.: Physiology, morphology and detailed passive models of guinea-pig cerebellar purkinje cells. Journal of Physiology (London) 474(1), 101–118 (1994)
Willshaw, D., Buneman, O., Longuet-Higgins, H.: Non-holographic associative memory. Nature 222, 960–962 (1969)
Steuber, V., De Schutter, E.: Long-term depression and recognition of parallel fibre patterns in a multi-compartmental model of a cerebellar purkinje cell. Neurocomputing 38, 383–388 (2001)
Dayan, P., Willshaw, D.J.: Optimising synaptic learning rules in linear associative memories. Biol. Cybern. 65(4), 253–265 (1991)
Graham, B.P.: Pattern recognition in a compartmental model of a ca1 pyramidal neuron. Network: Computation in Neural Systems 12(4), 473–492 (2001)
Isope, P., Barbour, B.: Properties of unitary granule cell-purkinje cell synapses in adult rat cerebellar slices. Journal of Neuroscience 22(22), 9668–9678 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
de Sousa, G., Adams, R., Davey, N., Maex, R., Steuber, V. (2009). The Effect of Different Forms of Synaptic Plasticity on Pattern Recognition in the Cerebellar Cortex. In: Kolehmainen, M., Toivanen, P., Beliczynski, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2009. Lecture Notes in Computer Science, vol 5495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04921-7_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-04921-7_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04920-0
Online ISBN: 978-3-642-04921-7
eBook Packages: Computer ScienceComputer Science (R0)