thereby transforms the discrete cone-opponent signals to a continuous representation of chromatic information. Such mixing mechanisms have been implemented by previous com-binatorial models of color processing, though through a largely feedforward approach or at the level of the single neuron. Our theory bears in mind the mixing mechanism, but reframes the stage-wise combina-torial scheme to one based on the nonlinear population dynamics within the visual cortex. Accordingly, we propose a hue-based cortical connectivity, built upon the cortical hue map micro-architecture revealed by recent optic al imaging studies of V1. By considering the intracortical network interactions, we have accounted for V1 cells responsive to the gamut of DKL directions without the need to fine-tune the cortical parameters. We do so without restricting to a particular category of V1 neuron, as both single-opponent and double-opponent, and altogether novel types of cells, have been suggested as the primary messen-gers of chromatic information. Rather, we zoom out from the individual neuron 's receptive field to model the aggregate, population-level properties and, in particular, the stable repre-sentation of hue. We thereby offer that chromatic processing in the visual cortex is, in itsessence, a self-organizing system of neuronal-activity pattern formation, capable of encoding chromatic information in the presence of visual stimuli and generating information in their absence. Further, in assuming modularity for chromatic processing, we have not ruled out a mech-anism for joint feature processing. Our choice to focus on the unoriented color cells within the CO blob regions allowed us to parse out the chromatic pathway for an independentanalysis, but should not be interpreted as a claim about its functional independence. We leave open the question of the functional and anatomical separation of the various visual streams. Equally unsettled is the question of how much S cone input contributes to the mixing of the cone-opponent channels, with some studie s showing a relatively weak S cone input into the neurons of V1, compared to its L and M cone counterparts ( Li et al., 2022 ;Xiao, 2014 ). The variations across these experiments may stem, in part, from differences in optical imaging and electrode penetration techniques, including the particulars of the chromatic stimulus used (Li et al., 2022 ;Liu et al., 2020 ;Polimeni, Granquist-Fraser, Wood, & Schwartz, 2005 ; Salzmann et al., 2012 ). On the whole, however, single-cell recordings have identified two main types of color-responsive regions: color patches that contain neurons tuned exclusively to stimuli modulating either of the cone-opponent pathways, and patches with neurons exhi-biting a mixed sensitivity to a combination of the two ( Landisman & Ts 'o, 2002a ;Li et al., 2022 ;Livingstone & Hubel, 1984 ). Further experiments on the connectivity between these regions, and among the single-and double-opponent color cell populations of which theyconsist, may point to added micro-architectures for the hue maps, along the lines of the geo-metric orientation models of Bressloff and Cowan (2003a) and Bressloff et al. (2001). Finally, we emphasize that the mechanism we offer departs from previous combinatorial color models that predict hue sensation at the final stage of processing ( De Valois & De Valois, 1993 ;Mehrani et al., 2020 ), as well as neural field models that conflate cone-and color-opponency in their interpretations ( Faugeras, Song, & Veltz, 2022 ;Smirnova, Chizhkova, & Chizhov, 2015 ;Song, Faugeras, & Veltz, 2019 ). The emergent hue tuning curves we have characterized are a network property reflective of the physiological neuronal responses, and should not be confounded with our perception of hue. A photon of wavelength 700 nm strik-ing a retina is no more “red”than any other particle —color is a perceptual phenomenon not yet represented in these first stages of vision. By recognizing that the hue tuning mechanism of Network Neuroscience 704Pattern forming mechanisms of color vision Downloaded from http://direct. mit. edu/netn/article-pdf/7/2/679/2118501/netn_a_00294. pdf by guest on 09 August 2024 