(Brown, 2014 ). Starting with cells in the retina and lateral geniculate nucleus (LGN) known to be tuned broadly to the cone-opponent axes, these proposed mechanisms build up to cells invarious cortical areas more narrowly tuned to divergent (and debated) chromatic directions in DKL space. While parsimonious, this formalism comes at the cost of tuning the cone weights arbitrarily, disregarding specific properties of real neurons 'receptive fields ( Eskew, 2009 ; Kaiser & Boynton, 1996 ;Stockman & Brainard, 2010 ). Furthermore, the linear combinatorial mechanism is not, on its own, able to account for the variety of color cells observed in the visual cortex ( Garg et al., 2019 ;Johnson, Hawken, & Shapley, 2001 ;Shapley & Hawken, 2011 ). In addition to the forward flow of chromatic information through the successive stages ofprocessing, the encoding of color reflects t he neuronal dynamics within each. Modelers agree that the next forays into a mechanistic theory of color vision should consider these intracortical circuits, but di sagree about where such interactions first become important (De Valois & De Valois, 1993 ;Hanazawa, Komatsu, & Murakami, 2000 ;Liu et al., 2020 ; Wachtler, Sejnowski, & Albright, 2003 ). Electrophysiological studies of macaque visual cortex have shed some light on this ques-tion, showing that the processing of individual hues previously associated with higher level mechanisms has its origins in the primary visual cortex (V1) ( Garg et al., 2019 ;Gegenfurtner, 2003 ;Hanazawa et al., 2000 ;Li et al., 2022 ;Wachtler et al., 2003 ;Xiao, 2014 ;Xiao, Casti, Xiao, & Kaplan, 2007 ). These experiments have identified the emergence of neurons in V1 tuned to the gamut of hues in DKL space, as well as to the role of processing nonlinearitiesin determining their tuning curves ( De Valois et al., 2000 ;Hanazawa et al., 2000 ;Lennie et al., 1990 ;Wachtler et al., 2003 ). Puzzlingly, these cells mainly inhabit the so-called CO “blobs, ” patchy regions rich in cytochrome oxidase that display a sensitivity to stimuli modulating either of the cone-opponent axes rather than the full set of hues ( Landisman & Ts 'o, 2002b ; Li et al., 2022 ;Livingstone & Hubel, 1984 ;Salzmann, Bartels, Logothetis, & Schüz, 2012 ). Some have speculated that this colocaliza tion stems from a mixing of cell populations encoding the two cardinal pathways (Li et al., 2022 ;Xiao, 2014 ) while others indicate a distinct population of hue-sensitive neurons in the “interblob ”regions, more conclusively associated with orientation tuning ( Garg et al., 2019 ;Landisman & Ts 'o, 2002a ). As a whole, however, these studies point to the need for a p opulation theory of chromatic processing remarkably early in the visual pathway. In this article, we present a model of color pr ocessing in which intracortical neuronal dynamics within V1 serve as the substrate for hue perception. Drawing on the canonical Wilson-Cowan neural field equations and the ring model of orientation tuning, we show that this population approach allows us to account for cells responsive to the full rangeof DKL directions without the need to fine-tune input parameters ( Ben-Yishai, Bar-Or, & Sompolinsky, 1995 ;Burstein, 2022 ;Hansel & Sompolinksy, 1998 ;Wilson & Cowan, 1972,1973 ). The threshholding we employ bears in mind the input-response nonlinearities of previous combinatorial models, but zooms out of the single-cell, feedforward interpreta-tion of input as the stimulus-driven LGN afferents to individual neurons. Rather, we model input as the total synaptic current into a population of cells, taking into account both thecone-opponent LGN afferents as well as the hue-dependent connectivity between distinct neuronal populations. The resulting demarcation between the cone-opponent and the hue-selective mechanisms in the same population of cells points to the importance of V1 in the transition from chromatic stimulus to color perception. To characterize this role, we study the effects of the model 's con-nectivity parameters and processing nonlinearities on the narrowness and stability of the hue tuning curves. In the final part of the paper, we show that the model is able to explain color Cardinal pathways: In DKL space, the two orthogonalaxes representing stimuli isolating the L-M and S-(L+M) cone-opponent pathways. Wilson-Cowan neural field equations: Coupled set of partial differentialequations describing the networkdynamics of excitatory and inhibitoryneural populations. Network Neuroscience 681Pattern 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 