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Inspired by these methods, we propose an approach to obtaining spatial and color tuning curves for convolutional neurons that can be used to classify cells in terms of their spatial and color opponency. We perform these classifications for a range of CNNs with different depths and bottleneck widths. Our key finding is that networks with a bottleneck show a strong functional organization: almost all cells in the bottleneck layer become both spatially and color opponent, and cells in the layer following the bottleneck become nonopponent. The color tuning data can further be used to form a rich understanding of how color a network encodes color. As a concrete demonstration, we show that shallower networks without a bottleneck learn a complex nonlinear color system, whereas deeper networks with tight bottlenecks learn a simple channel opponent code in the bottleneck layer. We develop a method of obtaining a hue sensitivity curve for a trained CNN that enables high-level insights that complement the low-level findings from the color tuning data. We go on to train a series of networks under different conditions to ascertain the robustness of the discussed results. Ultimately our methods and findings coalesce with prior art, strengthening our ability to interpret trained CNNs and furthering our understanding of the connection between architecture and learned representation. Trained models and code for all experiments are available at https:\/\/github.com\/ecs-vlc\/opponency.<\/jats:p>","DOI":"10.1162\/neco_a_01356","type":"journal-article","created":{"date-parts":[[2021,1,5]],"date-time":"2021-01-05T22:38:38Z","timestamp":1609886318000},"page":"858-898","update-policy":"http:\/\/dx.doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":1,"title":["How Convolutional Neural Network Architecture Biases Learned Opponency and Color Tuning"],"prefix":"10.1162","volume":"33","author":[{"given":"Ethan","family":"Harris","sequence":"first","affiliation":[{"name":"Vision Learning and Control, Electronics and Computer Science, University of Southampton, Southampton SO17 1B J, U.K., ewah1g13@ecs.soton.ac.uk"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniela","family":"Mihai","sequence":"additional","affiliation":[{"name":"Vision Learning and Control, Electronics and Computer Science, University of Southampton, Southampton SO17 1B J, U.K., adm1g15@ecs.soton.ac.uk"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonathon","family":"Hare","sequence":"additional","affiliation":[{"name":"Vision Learning and Control, Electronics and Computer Science, University of Southampton, Southampton SO17 1B J, U.K., jsh2@ecs.soton.ac.uk"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"281","published-online":{"date-parts":[[2021,3,26]]},"reference":[{"issue":"4","key":"2021041320044241100_B1","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1113\/jphysiol.1927.sp002410","article-title":"The action of light on the eye","volume":"63","author":"Adrian","year":"1927","journal-title":"Journal of Physiology"},{"issue":"3","key":"2021041320044241100_B2","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1113\/jphysiol.1927.sp002437","article-title":"The action of light on the eye","volume":"64","author":"Adrian","year":"1927","journal-title":"Journal of 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