Color categorization and color constancy in a neural network model of V4.
We develop a neural network model that instantiates color constancy and color categorization in a single unified framework. Previous models achieve similar effects but ignore important biological constraints. Color constancy in this model is achieved by a new application of the double opponent cells found in the "blobs" of the visual cortex. Color categorization emerges naturally, as a consequence of processing chromatic stimuli as vectors in a four-dimensional color space. A computer simulation of this model is subjected to the classic psychophysical tests that first uncovered these phenomena, and its response matches psychophysical results very closely.
Dufort
P A
PA
Department of Medicine, University of Toronto, Canada.
Lumsden
C J
CJ
D000818:Animals; D002540:Cerebral Cortex / Q000502:physiology; D003118:Color Perception; D006801:Humans; D008433:Mathematics; D016571:Neural Networks, Computer; D012165:Retinal Ganglion Cells / Q000502:physiology; D014795:Visual Pathways / Q000033:anatomy & histology / Q000502:physiology*