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Stochastic Ion Channel Gating and Probabilistic Computation in Dendritic Neurons

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The Computing Dendrite

Part of the book series: Springer Series in Computational Neuroscience ((NEUROSCI,volume 11))

Abstract

The electrical signals underlying neural computations are mediated by membrane ion channels. Although these ion channels are well known to operate stochastically, most computational models of dendritic neurons instead make the approximation that ionic conductances are deterministic. We review the basic mathematical considerations underlying this approximation and new efficient simulation tools that allow it to be evaluated systematically. We show how this approximation breaks down for dendritic neurons, with the relative functional influence of stochastic ion channel gating likely to depend strongly on neuron type. An important consequence of stochastic gating of ion channels may be that it causes dendritic neurons to integrate synaptic inputs probabilistically, rather than in the all or nothing fashion predicted by deterministic models.

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Authors and Affiliations

  1. Centre for Integrative Physiology, University of Edinburgh, Hugh Robson Building, 15 George Square, Edinburgh, EH8 9XD, UK

    Cian O’Donnell & Matthew F. Nolan

Authors
  1. Cian O’Donnell
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  2. Matthew F. Nolan
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Correspondence to Matthew F. Nolan .

Editor information

Editors and Affiliations

  1. Institute of Clinical Neuroanatomy, Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society & Goethe-University, Frankfurt, Germany

    Hermann Cuntz

  2. Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany

    Michiel W.H. Remme

  3. Department of Neurobiology, Hebrew University of Jerusalem, Jerusalem, Israel

    Benjamin Torben-Nielsen

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O’Donnell, C., Nolan, M.F. (2014). Stochastic Ion Channel Gating and Probabilistic Computation in Dendritic Neurons. In: Cuntz, H., Remme, M., Torben-Nielsen, B. (eds) The Computing Dendrite. Springer Series in Computational Neuroscience, vol 11. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8094-5_24

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