Acquisition of color opponent representation by a three-layered neural network model.

This paper discusses color representation in the visual system by analysis of a three-layered neural network model. The model incorporates physiological knowledge of color representation at the sensor level (broad-band trichromatic representation by cones) and the higher level (narrow-band color representation by color-coded cells in V4). We trained the model to perform a mapping between these color representations by the back propagation algorithm and analyzed the acquired characteristics of the hidden units. It turned out that the hidden units learned characteristics similar to those of the color opponent cells found in the visual system. It was concluded that the R-G and Y-B color opponent representations reflect the efficiency of the color representation in the visual system from investigations on the efficiency of color representation in the hidden layer and on the capability of the color recognition task of the model.