How can a pollinator, like the honey bee, perceive the same colors on visited flowers, despite continuous and rapid changes in ambient illumination and background color? A hundred years ago, von Kries proposed an elegant solution to this problem, color constancy, which is currently incorporated in many imaging and technological applications. However, empirical evidence on how this method can operate on animal brains remains tenuous. Our mathematical modeling proposes that the observed spectral tuning of simple ocellar photoreceptors in the honey bee allows for the necessary input for an optimal color constancy solution to most natural light environments. The model is fully supported by our detailed description of a neural pathway allowing for the integration of signals originating from the ocellar photoreceptors to the information processing regions in the bee brain. These findings reveal a neural implementation to the classic color constancy problem that can be easily translated into artificial color imaging systems.daylight | insect | vision | neuron tracing | von Kries C olor constancy allows natural and artificial visual systems to solve the challenge of maintaining the color sensation of an object despite changes in the illumination. Color constancy requires the visual system to discount an unknown illumination function, when the only available information to a sensor may be the total response of the different photoreceptors present in the visual system (1).Color constancy by chromatic adaptation is classically modeled by assuming a set of scalar coefficients that adjust the sensitivity of the different sensors responsible for color vision depending on the particular ambient light conditions (2, 3). The chromatic effect produced by variation of the spectral power distribution (SPD) of daylight is explained by differences in the ratio of long to short wavelengths observed at different conditions of daylight (4). This variability is constrained, potentially making possible the recovery of SPD from a limited number of sensor responses and simplifying color constancy solutions (5). Indeed, it is possible to reconstruct the entire SPD for different phases of daylight with just two functions summarizing daylight variability at short (<550 nm) and long (>550 nm) wavelengths (6). However, visual targets often present complex shapes and textures and are illuminated by variable intensity, spectrally mixed illumination, potentially requiring the use of contextual information for achieving color constancy (7,8). Chromatic adaptation is thus thought unlikely to be the sole mechanism enabling constancy (8, 9), and different cortical or other neural processing mechanisms seem important for reliable color vision (10, 11). Considering human vision, color processing involves multistage neural representations from V1 to V4 and the frontal cortex, and although V4 neurons seem critical for automatic color constancy operations (11-14), subsequent neural processing stages have been implicated (15).Although this evidence suggests ...