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Publication Detail
Spike-train variability of auditory neurons in vivo: dynamic responses follow predictions from constant stimuli.
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Comparative Study
  • Authors:
    Schaette R, Gollisch T, Herz AVM
  • Publication date:
    06/2005
  • Pagination:
    3270, 3281
  • Journal:
    J Neurophysiol
  • Volume:
    93
  • Issue:
    6
  • Status:
    Published
  • Country:
    United States
  • Print ISSN:
    0022-3077
  • PII:
    00758.2004
  • Language:
    eng
  • Keywords:
    Acoustic Stimulation, Action Potentials, Animals, Auditory Pathways, Dose-Response Relationship, Radiation, Grasshoppers, Models, Neurological, Neurons, Nonlinear Dynamics, Predictive Value of Tests, Reproducibility of Results, Time Factors
Abstract
Reliable accounts of the variability observed in neural spike trains are a prerequisite for the proper interpretation of neural dynamics and coding principles. Models that accurately describe neural variability over a wide range of stimulation and response patterns are therefore highly desirable, especially if they can explain this variability in terms of basic neural observables and parameters such as firing rate and refractory period. In this work, we analyze the response variability recorded in vivo from locust auditory receptor neurons under acoustic stimulation. In agreement with results from other systems, our data suggest that neural refractoriness has a strong influence on spike-train variability. We therefore explore a stochastic model of spike generation that includes refractoriness through a recovery function. Because our experimental data are consistent with a renewal process, the recovery function can be derived from a single interspike-interval histogram obtained under constant stimulation. The resulting description yields quantitatively accurate predictions of the response variability over the whole range of firing rates for constant-intensity as well as amplitude-modulated sound stimuli. Model parameters obtained from constant stimulation can be used to predict the variability in response to dynamic stimuli. These results demonstrate that key ingredients of the stochastic response dynamics of a sensory neuron are faithfully captured by a simple stochastic model framework.
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