Feb 4, 2020

A Bayesian Generative Model of Vestibular Afferent Neuron Spiking

bioRxiv
Michael PaulinKiri F. Pullar

Abstract

Using an information criterion to evaluate models fitted to spike train data from chinchilla semicircular canal afferent neurons, we found that the superficially complex functional organization of the canal nerve branch can be accurately quantified in an elegant mathematical model with only three free parameters. Spontaneous spike trains are samples from stationary renewal processes whose interval distributions are Exwald distributions, convolutions of Inverse Gaussian and Exponential distributions. We show that a neuronal membrane compartment is a natural computer for calculating parameter likelihoods given samples from a point process with such a distribution, which may facilitate fast, accurate, efficient Bayesian neural computation for estimating the kinematic state of the head. The model suggests that Bayesian neural computation is an aspect of a more general principle that has driven the evolution of nervous system design, the energy efficiency of biological information processing.

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Mentioned in this Paper

Head
Neuronal Cell Body Membrane
Neurons
Nervous System Structure
Afferent Neuron
Spinal Nerve Branch
Evaluation
Inversion

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