Spatial and colour opponency in anatomically constrained deep networks

Abstract

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.

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Identifiers

ORCID for Ethan William Albert Harris: orcid.org/0000-0003-3545-1349

ORCID for Andreea Daniela Mihai: orcid.org/0000-0003-3368-9062

ORCID for Jonathon Hare: orcid.org/0000-0003-2921-4283

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