 Solution

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.^[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 asThis 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.^[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.^[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. Trichromatic sampling limits spectral information; later processing determines how that information supports boundaries, stable surface appearance and behavior.