TY - JOUR
T1 - Differential Neural Network-Based Nonparametric Identification of Eye Response to Enforced Head Motion
AU - Chairez, Isaac
AU - Mukhamedov, Arthur
AU - Prud, Vladislav
AU - Andrianova, Olga
AU - Chertopolokhov, Viktor
N1 - Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/3/1
Y1 - 2022/3/1
N2 - 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.
AB - 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.
KW - Artificial neural network
KW - Control Lyapunov function
KW - Izhikevich artificial neuron
KW - Nonparametric model
KW - Vestibular– ocular reflex
UR - http://www.scopus.com/inward/record.url?scp=85126292547&partnerID=8YFLogxK
U2 - 10.3390/math10060855
DO - 10.3390/math10060855
M3 - Artículo
AN - SCOPUS:85126292547
SN - 2227-7390
VL - 10
JO - Mathematics
JF - Mathematics
IS - 6
M1 - 855
ER -