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

Author

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  • Isaac Chairez

    (Bioprocesses Department, UPIBI, Instituto Politecnico Nacional, Ciudad de Mexico 07340, Mexico
    School of Engineering, Tecnologico de Monterrey, Campus Guadalajara, Monterrey 64849, Mexico)

  • Arthur Mukhamedov

    (Center “Supersonic”, Lomonosov Moscow State University, 119991 Moscow, Russia)

  • Vladislav Prud

    (Center “Supersonic”, Lomonosov Moscow State University, 119991 Moscow, Russia)

  • Olga Andrianova

    (V.A. Trapeznikov Institute of Control Sciences of RAS, 117997 Moscow, Russia)

  • Viktor Chertopolokhov

    (Center “Supersonic”, Interdisciplinary Scientific and Educational School “Mathematical Methods of Large-Scale Systems Analysis”, Lomonosov Moscow State University, 119991 Moscow, Russia)

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.

Suggested Citation

  • Isaac Chairez & Arthur Mukhamedov & Vladislav Prud & Olga Andrianova & Viktor Chertopolokhov, 2022. "Differential Neural Network-Based Nonparametric Identification of Eye Response to Enforced Head Motion," Mathematics, MDPI, vol. 10(6), pages 1-12, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:855-:d:766807
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    References listed on IDEAS

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    1. Kumar, Ankit & Das, Subir & Yadav, Vijay K. & Rajeev,, 2021. "Global quasi-synchronization of complex-valued recurrent neural networks with time-varying delay and interaction terms," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
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    Cited by:

    1. Natalia Bakhtadze, 2023. "Preface to the Special Issue on “Identification, Knowledge Engineering and Digital Modeling for Adaptive and Intelligent Control”—Special Issue Book," Mathematics, MDPI, vol. 11(8), pages 1-3, April.
    2. Francisco Beltran-Carbajal & Hugo Yañez-Badillo & Ruben Tapia-Olvera & Julio C. Rosas-Caro & Carlos Sotelo & David Sotelo, 2023. "Neural Network Trajectory Tracking Control on Electromagnetic Suspension Systems," Mathematics, MDPI, vol. 11(10), pages 1-26, May.

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