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Human optokinetic nystagmus: a stochastic analysis.

Jonathan Waddington¹, Christopher M Harris

¹Centre for Robotics and Neural Systems and Plymouth Cognition Institute, Plymouth University, Plymouth, UK. jonathan.waddington@plymouth.ac.uk

Journal of Vision

|November 10, 2012

Summary

Optokinetic nystagmus (OKN) variability stems from noise, not changing relationships. A new model reveals three noise sources impacting eye movements during this fundamental gaze-stabilizing response.

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Area of Science:

Neuroscience
Vision Science
Biophysics

Background:

Optokinetic nystagmus (OKN) is a crucial gaze-stabilizing reflex in vertebrates.
OKN involves alternating slow and fast eye movements to counteract self-motion optic flow.
The variability in OKN timing and amplitude remains poorly understood.

Purpose of the Study:

To investigate the sources of variability in optokinetic nystagmus (OKN).
To develop a quantitative model for OKN dynamics.
To understand the role of noise in OKN generation.

Main Methods:

Principal components analysis (PCA) of OKN data.
Development of a linear stochastic model for OKN.
Analysis of slow phase (SP) velocity, quick phase (QP) trigger, and QP amplitude.

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Main Results:

PCA revealed three main components explaining OKN variance, primarily within single cycles.
Variability correlated with system noise, while underlying relationships remained stable.
A triple first-order Markov process model was developed, incorporating three noise sources.

Conclusions:

OKN variability is largely attributable to signal-dependent noise.
The model predicts noise levels, transient state durations, and undershoot biases.
This provides a framework for understanding the neural control of gaze stabilization.