= Solution
\b[Post-receptoral processing compares cone signals, organizes those comparisons across space and time, and uses them to represent surfaces and objects. Three receptor classes do not imply three <neurons> labelled with three <color> names.] A complete answer must distinguish the information available at the receptors from its neural transformation and its perceptual interpretation.
The <principle of univariance> supplies the starting constraint. For one cone class, an absorption-dependent response does not identify <wavelength> independently of <intensity>. Comparing differently sensitive classes makes chromatic information available. Relative to an adapted background, cone contrasts can be written $c_j=(Q_j-Q_{j0})/Q_{j0}$, where $Q_{j0}>0$ is the background catch. This representation separates a change relative to the prevailing input from the raw number of absorbed <photons>. It is a useful coordinate system, not a claim that every <neuron> implements this exact formula.
Retinal circuitry immediately transforms the signals. <Light> hyperpolarizes <cone cells> and changes their transmitter release. ON and OFF bipolar pathways respond differently to changes in cone output, while horizontal and amacrine circuitry contribute spatial and temporal interactions. <Retinal ganglion cells> transmit <spike trains> rather than simply forwarding a cone's voltage. Their center–surround organization compares regions, so chromatic and spatial structure are already intertwined. Near the central <retina>, the midget pathway can oppose an L- or M-dominated center to a surround containing other cone input. The same circuitry can carry information useful for fine spatial structure and for chromatic contrast; these are not necessarily separate wires.
A distinct S-cone route illustrates the anatomical specificity. <Dennis M. Dacey> and colleagues combined physiological characterization with identification of ganglion-cell morphology in primate <retina>, finding that the S-ON opponent response belonged to a small bistratified ganglion-cell type rather than simply another midget cell.<The blue-on opponent pathway in primate retina originates from a distinct bistratified ganglion cell type by Dennis M. Dacey and others>[Nature 367, 731–735; physiological and morphological identification]{ref} This supports parallel circuits with different cone combinations. Calling the pathway “blue-on” is shorthand for its stimulus response, not proof that the cell itself produces a unique blue experience.
At a simplified computational level, an achromatic-like combination and two chromatic combinations can be represented as
$$
y_0=c_L+c_M,\qquad y_1=c_L-c_M,\qquad y_2=c_S-\tfrac12(c_L+c_M).
$$
This <invertible three-channel opponent transform> reorganizes three coordinates rather than creating additional independent spectral information. Their <matrix> has <determinant> $-2$, so the transform is invertible: $c_L=(y_0+y_1)/2$, $c_M=(y_0-y_1)/2$ and $c_S=y_2+y_0/2$. Actual weights depend on cell, location, adaptation and stimulus conditions. This algebra is an illustrative opponent transform, not a fitted retinal wiring diagram or a complete perceptual <color space>.
The <lateral geniculate nucleus> preserves several parallel retinal pathways while also participating in cortical interactions. <A. M. Derrington> and colleagues studied macaque geniculate cells with gratings whose <luminance> and chromatic modulation could be varied independently on a calibrated <color> television display. Finding directions that changed cone stimulation without driving a cell constrained its linear cone weights.<Chromatic mechanisms in lateral geniculate nucleus of macaque by A. M. Derrington and others>[J Physiol 357, 241–265; null-plane method and chromatic stimulus space]{ref} For a nonzero linear weight vector, the zero-response contrasts obey $r=\mathbf w^{\mathsf T}\mathbf c=0$, defining a <chromatic null plane>. A null response therefore tests a relation between inputs, rather than merely assigning the cell a preferred <color> name. The resulting opponent directions need not coincide with human <unique hues>.
Cortical processing adds more complex spatial comparisons. <Bevil R. Conway> mapped cone inputs to color-responsive <neurons> in alert macaque V1 using cone-isolating spots and eye-position-corrected reverse correlation. Some cells combined chromatic opponency with opposite tuning in adjacent spatial subregions.<Spatial Structure of Cone Inputs to Color Cells in Alert Macaque Primary Visual Cortex by Bevil R. Conway>[J Neurosci 21, 2768–2783; receptive-field mapping and double opponency]{ref} Such <double-opponent cells> can respond strongly to a chromatic boundary while responding less to a spatially uniform change. This offers a mechanism useful for separating surface differences from shared illumination changes. It does not establish that one V1 population alone solves <color constancy> under arbitrary scenes.
Further processing must relate <color> to form, illumination, material and the current task. The same cone contrasts can be interpreted differently in a shadow, highlight or translucent object. Surface <color> judgments, discrimination thresholds and <color naming> therefore constrain different computations. Feedback and <attention> can influence which information is selected, while <learning> can alter how a signal is interpreted or normalized. A lesion or imaging response can constrain contributions to those tasks, but an area responsive to <color> is not automatically an isolated “<color> center.”
The strongest picture is consequently a hierarchy of interacting computations: cone-specific signals, opponent and spatial filtering, combinations tuned to richer image structure, and object-level judgments. \b[Trichromatic sampling limits spectral information; later processing determines how that information supports boundaries, stable surface appearance and behavior.]
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