Differential Neural Network-Based Nonparametric Identification of Eye Response to Enforced Head Motion

Isaac Chairez,Arthur Mukhamedov,Viktor Chertopolokhov,Vladislav Prud,Olga Andrianova

doi:10.3390/math10060855

Abstract

Dynamic motion simulators cannot provide the same stimulation of sensory systems as real motion. Hence, only a subset of human senses should be targeted. For simulators providing vestibular stimulus, an automatic bodily function of vestibular–ocular reflex (VOR) can objectively measure how accurate motion simulation is. This requires a model of ocular response to enforced accelerations, an attempt to create which is shown in this paper. The proposed model corresponds to a single-layer spiking differential neural network with its activation functions are based on the dynamic Izhikevich model of neuron dynamics. An experiment is proposed to collect training data corresponding to controlled accelerated motions that produce VOR, measured using an eye-tracking system. The effectiveness of the proposed identification is demonstrated by comparing its performance with a traditional sigmoidal identifier. The proposed model based on dynamic representations of activation functions produces a more accurate approximation of foveal motion as the estimation of mean square error confirms.

Highlights

Based on the described approximate dynamical model, this study considers a model for uncertain dynamics of the vestibular–ocular reflex (VOR) based on the design of an adaptive SDNN
This study examines modeling physiological VOR systems using SDNN
The proposed nonparametric model implements an arrangement of the artificial neurons described by Izhikevich dynamics with fixed parameters to follow eye movements caused by known head accelerations

Summary

Introduction

A significant multidiscipline effort deals with developing technologies that can be applied for training in simulated environments. It is called the vestibular–ocular reflex (VOR), and it is one of the interaction processes between a human body and the surrounding environment It operates via a neural path between the vestibular and oculomotor systems: eyes compensate head rotations by rotating in the opposite direction [4]. A natural way to study VOR is to observe it using immersive technologies (such as virtual or mixed reality) and produce reliable and accurate mathematical models of VOR with human motion as input and electrophysiological response as output. A new aggregated system is used to confirm the validity of the proposed model It consists of an experimental system with a motion platform, inertial sensors, an eye-tracking device for acquiring data, and a neural network for processing it.

Description of Vestibular–Ocular Connection

Modeling Ocular Response to Enforced Acceleration

Formulation of Spiking-Differential-Neural-Network-Based Model

Modeling Process and Experimental Validation

Numerical Simulation

Conclusions

Findings

Patents