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The retina still poses many structural and computational questions. Structurally, for example, it is not yet clear how many distinct horizontal cell (HC) types the primate retina contains and what the exact patterns of connections between photoreceptors (PRs) and HCs consist of. Computationally, it is not yet clear, for instance, what functions are present and how they are being implemented. This paper proposes a model (a linear recurrent neural network defined by 31 parameters) of the outer retina and an optimization methodology that hopes to shed some light on these questions. This paper shows that a simplified model of the outer retina can implement several low-level visual functions involving the modulation of noise, brightness, contrast, saturation, and even color. The results demonstrate that contrast control functions can be implemented with a minimum of two HC types and that spectral specificity between PRs and HCs is a common and important feature. It is also shown that several different spectrally specific patterns can emerge in order to implement the same function. One interesting microcircuit that naturally emerged from our experiments involves nonblurry denoising via interchromatic gap junctions and compensatory resaturation via HC circuits, a strategy that we hypothesize to exist in some biological retinae.