Model of neural visual system with self-organizing cells.

This paper describes a model of a neural visual system of a higher animal, in which the capability of pattern recognition develops adaptively. To produce the adaptability, we adopted "self-organizing cells," and with them modeled feature-detecting cells which were discovered by Hubel and Wiesel and whose plasticity was found by Blakemore and Cooper. Combining the "self-organizing cells" and the learning principle of a Perceptron-type system, we constructed a model of the whole visual system. The model is also equipped with an eye movement control mechanism for gazing, which reduces the number of "self-organizing cells" required for pattern recognition, thus contributing to their quick self-organization. Computer simulation and an experiment using a hardware simulator showed that "self-organizing cells" quickly become sensitive to the features often seen and that the resulted system can classify patterns with a rather small number of feature-detecting cells.