So they argue a very interesting point: in the context of color vision, the typical decoding methods pick out a single value. But when you see white you are seeing the combination of several wavelengths, and the color you experience is separate from the individual wavelengths. Essentially this generalizes to mixtures of stimuli, where typically population codes account for only a single stimulus value. Complex stimuli are represented in a different way than just their average or maximum likelihood estimate, even a multi-modal estimate may not be correct.
White can be thought of as an arbitrary symbol or label marking the presence of a certain combination of color-tuning curve activities. Decoding is not specifying a single physical parameter, but to specifying some region in an abstract psychological space. There is a straight-forward linear transformation from wavelength space (or cone activation space) to 2D CIE color space (which is like HSV). (The mapping actually requires division to compute). Brightness is the 3rd dimension and is represented by absolute levels of activity -- gray and white map to same CIE point, but differ in brightness.
They make a 4 layer NN to get from cones to CIE. Layer 2 is color opponent units "+r-g" which are similar to RGCs. Layer 3 is noncolor opponent and they make a comparison to neurons in V4, this was just the hidden layer of the network.
This, in a way, is just mapping from one population-code space (wavelengths) to another space (CIE color). This is a symbolic representation of a set of variables, which is likely more behaviorally useful (thus is why this system evolved).
No comments:
Post a Comment