A neural network model of the binocular fusion in the human vision

This paper proposes a model of binocular fusion based on psychological experimental results and physiological knowledge. Considering the psychological results and the physiological structure, the authors assume that the binocular information is processed by several binocular channels having different spatial characteristics from low spatial frequency to high spatial frequency. In order to examine the mechanism of binocular fusion, the authors construct a five layer neural network model, and train it by the backpropagation learning algorithm using psychological experimental data. After completion of learning, the generalization capability of the network is examined. Further, the response functions of the hidden units have been examined, which suggested that the hidden units have a spatial selective characteristic.<>