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Deep learning models reveal internal structure and diverse computations in the retina under natural scenes

View ORCID ProfileNiru Maheswaranathan, Lane McIntosh, David B. Kastner, Josh Melander, Luke Brezovec, Aran Nayebi, Julia Wang, Surya Ganguli, Stephen A. Baccus
doi: https://doi.org/10.1101/340943
Niru Maheswaranathan
Neuroscience Program, Stanford University School of Medicine, Stanford, CA
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Lane McIntosh
Neuroscience Program, Stanford University School of Medicine, Stanford, CA
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David B. Kastner
Neuroscience Program, Stanford University School of Medicine, Stanford, CA
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Josh Melander
Neuroscience Program, Stanford University School of Medicine, Stanford, CA
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Luke Brezovec
Neuroscience Program, Stanford University School of Medicine, Stanford, CA
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Aran Nayebi
Neuroscience Program, Stanford University School of Medicine, Stanford, CA
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Julia Wang
Stanford University, Stanford, CA
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Surya Ganguli
Department of Applied Physics, Stanford University, Stanford, CA
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Stephen A. Baccus
Department of Neurobiology, Stanford University, Stanford, CA
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Abstract

The normal function of the retina is to convey information about natural visual images. It is this visual environment that has driven evolution, and that is clinically relevant. Yet nearly all of our understanding of the neural computations, biological function, and circuit mechanisms of the retina comes in the context of artificially structured stimuli such as flashing spots, moving bars and white noise. It is fundamentally unclear how these artificial stimuli are related to circuit processes engaged under natural stimuli. A key barrier is the lack of methods for analyzing retinal responses to natural images. We addressed both these issues by applying convolutional neural network models (CNNs) to capture retinal responses to natural scenes. We find that CNN models predict natural scene responses with high accuracy, achieving performance close to the fundamental limits of predictability set by intrinsic cellular variability. Furthermore, individual internal units of the model are highly correlated with actual retinal interneuron responses that were recorded separately and never presented to the model during training. Finally, we find that models fit only to natural scenes, but not white noise, reproduce a range of phenomena previously described using distinct artificial stimuli, including frequency doubling, latency encoding, motion anticipation, fast contrast adaptation, synchronized responses to motion reversal and object motion sensitivity. Further examination of the model revealed extremely rapid context dependence of retinal feature sensitivity under natural scenes using an analysis not feasible from direct examination of retinal responses. Overall, these results show that nonlinear retinal processes engaged by artificial stimuli are also engaged in and relevant to natural visual processing, and that CNN models form a powerful and unifying tool to study how sensory circuitry produces computations in a natural context.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted June 08, 2018.
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Deep learning models reveal internal structure and diverse computations in the retina under natural scenes
Niru Maheswaranathan, Lane McIntosh, David B. Kastner, Josh Melander, Luke Brezovec, Aran Nayebi, Julia Wang, Surya Ganguli, Stephen A. Baccus
bioRxiv 340943; doi: https://doi.org/10.1101/340943
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Deep learning models reveal internal structure and diverse computations in the retina under natural scenes
Niru Maheswaranathan, Lane McIntosh, David B. Kastner, Josh Melander, Luke Brezovec, Aran Nayebi, Julia Wang, Surya Ganguli, Stephen A. Baccus
bioRxiv 340943; doi: https://doi.org/10.1101/340943

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