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 selforganizing 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.