Skip to main content

ORIGINAL RESEARCH article

Front. Neurol.
Sec. Neuro-Otology
Volume 15 - 2024 | doi: 10.3389/fneur.2024.1465211
This article is part of the Research Topic Computational Neuroscience Approaches in Neuro-Otology View all 11 articles

Towards the Bayesian Brain: A generative model of information transmission by vestibular sensory neurons

Provisionally accepted
Mike G. Paulin Mike G. Paulin 1Kiri F Pullar Kiri F Pullar 1Larry F. Hoffman Larry F. Hoffman 2*
  • 1 Department of Zoology, University of Otago, Dunedin 9016, New Zealand
  • 2 Department of Head & Neck Surgery, Brain Research Institute, Geffen School of Medicine at UCLA, Los Angeles, California, United States

The final, formatted version of the article will be published soon.

    The relative accessibility and simplicity of vestibular sensing and vestibular-driven control of head and eye movements has made the vestibular system an attractive subject to experimenters and theoreticians interested in developing realistic quantitative models of how brains gather and interpret sense data and use it to guide behavior. Head stabilization and eye counter-rotation driven by vestibular sensory input in response to rotational perturbations represent natural, ecologically important behaviors that can be reproduced in the laboratory and analyzed using relatively simple mathematical models. Models drawn from dynamical systems and control theory have previously been used to analyze the behavior of vestibular sensory neurons. In the Bayesian framework, which is becoming widely used in cognitive science, vestibular sense data must be modelled as random samples drawn from probability distributions whose parameters are kinematic state variables of the head. We show that Exwald distributions are accurate models of spontaneous interspike interval distributions in spike trains recoded from chinchilla semicircular canal afferent neurons. Each interval in an Exwald distribution is the sum of an interval drawn from an Exponential distribution and a Wald or Inverse Gaussian distribution. We show that this abstract model can be realized using simple physical mechanisms and re-parameterized in terms of the relevant kinematic state variables of the head. This model predicts and explains statistical and dynamical properties of semicircular canal afferent neurons in a novel way. It provides an empirical foundation for realistic Bayesian models of neural computation in the brain that underlie the perception of head motion and the control of head and eye movements.

    Keywords: hair cell receptor, spike train analysis, Neural coding, Stochastic point process, Computational neural model, perception as Bayesian inference, Cerebellum, cerebellar-like

    Received: 15 Jul 2024; Accepted: 18 Nov 2024.

    Copyright: © 2024 Paulin, Pullar and Hoffman. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Larry F. Hoffman, Department of Head & Neck Surgery, Brain Research Institute, Geffen School of Medicine at UCLA, Los Angeles, California, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.