Neural network mosaic model for pupillary responses to spatial stimuli |
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Authors: | Wei Sun Fuchuan Sun George Hung |
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Institution: | (1) Department of Biocybernetics and Biomedical Engineering, Shanghai Institute of Physiology, Chinese Academy of Sciences, 320 Yo-yang Road Shanghai 200031, China; Laboratory of Visual Information Processing of Institute of biophysics and Laboratory of Neurobiology of Shanghai Institute of Physiology, Academia Sinica, CN;(2) Department of Biomedical Engineering, Rutgers University, 617 Bowser Road, Piscataway, NJ 08854–8014, USA, US |
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Abstract: | A neural network mosaic model was developed to investigate the spatial-temporal properties of the human pupillary control
system. It was based on the double-layer neural network model developed by Cannon and Robinson and the pupillary dual-path
model developed by Sun and Stark. The neural network portion of the model received its input from a sensor array and consisted
of a retina-like two-dimensional neuronal layer. The dual-path portion of the model was composed of interconnections of the
neurons that formed a mosaic of AC transient and DC sustained paths. The spatial aggregates of the AC and DC signals were
input to the AC and DC summing neurons, respectively. Finally, the weighted sum of the aggregate AC and DC signals provided
the output for driving the pupillary response. An important property of the model was that it could adaptively learn from
training samples by adjustment of the weights. The neural network mosaic model showed excellent performance in simulating
both the traditional pupillary phenomena and the new spatial stimulation findings such as responses to change in stimulus
pattern and shift of light spot. Moreover, the model could also be used for the diagnosis of clinical deficits and image processing
in machine vision.
Received: 12 December 1997 / Accepted in revised form: 22 April 1998 |
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