Wide-field motion integration in fly VS cells: insights from an inverse approach

Ben Torben-Nielsen, Klaus M. Stiefel

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)
    34 Downloads (Pure)

    Abstract

    Fly lobula plate tangential cells are known to perform wide-field motion integration. It is assumed that the shape of these neurons, and in particular the shape of the subclass of VS cells, is responsible for this type of computation. We employed an inverse approach to investigate the morphology-function relationship underlying wide-field motion integration in VS cells. In the inverse approach detailed, model neurons are optimized to perform a predefined computation: here, wide-field motion integration. We embedded the model neurons to be optimized in a biologically plausible model of fly motion detection to provide realistic inputs, and subsequently optimized model neuron with and without active conductances (g(Na), g(K), g(K(Na))) along their dendrites to perform this computation. We found that both passive and active optimized model neurons perform well as wide-field motion integrators. In addition, all optimized morphologies share the same blueprint as real VS cells. In addition, we also found a recurring blueprint for the distribution of g(K) and g(Na) in the active models. Moreover, we demonstrate how this morphology and distribution of conductances contribute to wide-field motion integration. As such, by using the inverse approach we can predict the still unknown distribution of g(K) and g(Na) and their role in motion integration in VS cells

    Original languageEnglish
    Article numbere1000932
    Number of pages11
    JournalPLoS Computational Biology
    Volume6
    Issue number9
    DOIs
    Publication statusPublished - 2010

    Keywords

    • Algorithms
    • Animals
    • Computational Biology
    • Computer Simulation
    • Dendrites
    • Diptera
    • Electrophysiology
    • Ion Channels
    • Models, Neurological
    • Motion
    • Neurons
    • Visual Fields

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