Model for a color perception system with learning capabilities

We present a model for color vision system with learning capabilities. The system adapts to statistical properties of its input. The adaptation is done by utilizing unsupervised learning techniques, as self-organizing feature maps and vectorial boundary adaptation maps. A color difference reflecting statistical properties of input to the system is defined. The model was tested by using color data with different statistics and two different sets of rhodopsin- based color sensors.