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Simulation of white-light adaptation characteristics with use of nonlinear neural principal component analysis

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Abstract

We use a nonlinear neural principal component analysis to approximate and simulate white-light adaptation characteristics of opponent color signals and an achromatic signal. The algorithm used was derived from Sanger’s generalized Hebbian algorithm [Neural Netw. 2, 459 (1989)] with use of nonlinearities in short-wavelength cone outputs. The principal components can be interpreted as one achromatic and two opponent color signals. Simulation examples show that the algorithm can approximate opponent color signals and adaptation characteristics for the red–green signal that closely resemble those reported in the literature. The model used incorporates nonlinear models of the opponent mechanism with white-light adaptation characteristics and allows a nonlinear adaptable interaction between opponent mechanisms.

© 1997 Optical Society of America

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