Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany; Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
Klaudia P Szatko
Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany; Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
Yuyao Deng
Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany; Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
Yongrong Qiu
Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany; Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
David Alexander Klindt
SLAC National Accelerator Laboratory, Stanford University, Menlo Park, United States
Zachary Jessen
Feinberg School of Medicine, Department of Ophthalmology, Northwestern University, Chicago, United States
Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany; Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany; Tübingen AI Center, University of Tübingen, Tübingen, Germany; Hertie Institute for AI in Brain Health, Tübingen, Germany
Institute of Computer Science and Campus Institute Data Science, University of Göttingen, Göttingen, Germany; Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany; Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
The retina transforms patterns of light into visual feature representations supporting behaviour. These representations are distributed across various types of retinal ganglion cells (RGCs), whose spatial and temporal tuning properties have been studied extensively in many model organisms, including the mouse. However, it has been difficult to link the potentially nonlinear retinal transformations of natural visual inputs to specific ethological purposes. Here, we discover a nonlinear selectivity to chromatic contrast in an RGC type that allows the detection of changes in visual context. We trained a convolutional neural network (CNN) model on large-scale functional recordings of RGC responses to natural mouse movies, and then used this model to search in silico for stimuli that maximally excite distinct types of RGCs. This procedure predicted centre colour opponency in transient suppressed-by-contrast (tSbC) RGCs, a cell type whose function is being debated. We confirmed experimentally that these cells indeed responded very selectively to Green-OFF, UV-ON contrasts. This type of chromatic contrast was characteristic of transitions from ground to sky in the visual scene, as might be elicited by head or eye movements across the horizon. Because tSbC cells performed best among all RGC types at reliably detecting these transitions, we suggest a role for this RGC type in providing contextual information (i.e. sky or ground) necessary for the selection of appropriate behavioural responses to other stimuli, such as looming objects. Our work showcases how a combination of experiments with natural stimuli and computational modelling allows discovering novel types of stimulus selectivity and identifying their potential ethological relevance.
