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

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

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish
Article number855
JournalMathematics
Volume10
Issue number6
DOIs
StatePublished - 1 Mar 2022

Keywords

  • Artificial neural network
  • Control Lyapunov function
  • Izhikevich artificial neuron
  • Nonparametric model
  • Vestibular– ocular reflex

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