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Neuronal identification of signal periodicity by balanced inhibition

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Abstract

Many animals, including men, use periodicity information, e.g., amplitude modulations of acoustic stimuli, as a vital cue to auditory object formation. The underlying neuronal mechanisms, however, still remain a matter of debate. Here, we mathematically analyze a model for periodicity identification that relies on the interplay of excitation and delayed inhibition. Our analytical results show how the maximal response of such a system varies systematically with the time constants of excitation and inhibition. The model reliably identifies signal periodicity in the range from about ten to several hundred Hertz. Importantly, the model relies on biologically plausible parameters only. It works best for excitatory and inhibitory neuronal couplings of equal strength, the so-called ‘balanced inhibition’ We show how balanced inhibition can serve to identify low-frequency signal periodicity and how variation of a single parameter, the inhibitory time constant, can tune the system to different frequencies.

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Authors and Affiliations

  1. Physik Department T35, Technische Universität München, 85748, Garching bei München, Germany

    Moritz Bürck & J. Leo van Hemmen

  2. Bernstein Center for Computational Neuroscience, Munich, Germany

    Moritz Bürck & J. Leo van Hemmen

Authors
  1. Moritz Bürck
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  2. J. Leo van Hemmen
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Correspondence to Moritz Bürck.

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Bürck, M., van Hemmen, J.L. Neuronal identification of signal periodicity by balanced inhibition. Biol Cybern 100, 261–270 (2009). https://doi.org/10.1007/s00422-009-0302-0

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  • DOI: https://doi.org/10.1007/s00422-009-0302-0

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