  Colour vision has long fascinated scientists, who have sought to understand
both the physiology of the mechanics of colour vision and the psychophysics of
colour perception. We consider representations of colour in anatomically
constrained convolutional deep neural networks. Following ideas from
neuroscience, we classify cells in early layers into groups relating to their
spectral and spatial functionality. We show the emergence of single and double
opponent cells in our networks and characterise how the distribution of these
cells changes under the constraint of a retinal bottleneck. Our experiments not
only open up a new understanding of how deep networks process spatial and
colour information, but also provide new tools to help understand the black box
of deep learning. The code for all experiments is avaialable at
\url{https://github.com/ecs-vlc/opponency}.
