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.
Department of Medicine, University of Toronto, Canada.
P A
PA
Dufort
C J
CJ
Lumsden
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*