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The dynamic neural code of the retina for natural scenes

View ORCID ProfileNiru Maheswaranathan, Lane T. McIntosh, Hidenori Tanaka, Satchel Grant, David B. Kastner, Josh B. Melander, Aran Nayebi, Luke Brezovec, Julia Wang, Surya Ganguli, Stephen A. Baccus
doi: https://doi.org/10.1101/340943
Niru Maheswaranathan
Neuroscience Program, Stanford University School of Medicine, Stanford, CAGoogle Brain, Mountain View, CA
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  • ORCID record for Niru Maheswaranathan
Lane T. McIntosh
Neuroscience Program, Stanford University School of Medicine, Stanford, CATesla, Inc., Palo Alto, CA
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Hidenori Tanaka
Department of Applied Physics, Stanford University, Stanford, CAPhysics & Informatics Laboratories, NTT Research, Inc., East Palo Alto, CA, USA
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Satchel Grant
Department of Neurobiology, Stanford University, Stanford, CA
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David B. Kastner
Neuroscience Program, Stanford University School of Medicine, Stanford, CADepartment of Psychiatry, University of California, San Francisco, CA
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Josh B. Melander
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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Luke Brezovec
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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  • For correspondence: baccus@stanford.edu
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Abstract

Understanding how the visual system encodes natural scenes is a fundamental goal of sensory neuroscience. We show here that a three-layer network model predicts the retinal response to natural scenes with an accuracy nearing the fundamental limits of predictability. The model’s internal structure is interpretable, in that model units are highly correlated with interneurons recorded separately and not used to fit the model. We further show the ethological relevance to natural visual processing of a diverse set of phenomena of complex motion encoding, adaptation and predictive coding. Our analysis uncovers a fast timescale of visual processing that is inaccessible directly from experimental data, showing unexpectedly that ganglion cells signal in distinct modes by rapidly (< 0.1 s) switching their selectivity for direction of motion, orientation, location and the sign of intensity. A new approach that decomposes ganglion cell responses into the contribution of interneurons reveals how the latent effects of parallel retinal circuits generate the response to any possible stimulus. These results reveal extremely flexible and rapid dynamics of the retinal code for natural visual stimuli, explaining the need for a large set of interneuron pathways to generate the dynamic neural code for natural scenes.

Footnotes

  • Updated author current addresses. Additional analysis are made analyzing the dynamic receptive fields of the retina, including the report of novel properties of changing direction selectivity, orientation selectivity and spatiotemporal sensitivity (Fig. 5). In addition a new analysis identifying the contribution of internal units to the output of the model is performed, showing that natural scenes engages a larger set of neural pathways than white noise (Fig. 6).

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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 December 17, 2019.
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The dynamic neural code of the retina for natural scenes
Niru Maheswaranathan, Lane T. McIntosh, Hidenori Tanaka, Satchel Grant, David B. Kastner, Josh B. Melander, Aran Nayebi, Luke Brezovec, Julia Wang, Surya Ganguli, Stephen A. Baccus
bioRxiv 340943; doi: https://doi.org/10.1101/340943
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The dynamic neural code of the retina for natural scenes
Niru Maheswaranathan, Lane T. McIntosh, Hidenori Tanaka, Satchel Grant, David B. Kastner, Josh B. Melander, Aran Nayebi, Luke Brezovec, Julia Wang, Surya Ganguli, Stephen A. Baccus
bioRxiv 340943; doi: https://doi.org/10.1101/340943

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