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Predictability in natural images determines V1 firing rates and synchronization: A deep neural network approach

Cem Uran, View ORCID ProfileAlina Peter, Andreea Lazar, William Barnes, Johanna Klon-Lipok, Katharine A Shapcott, Rasmus Roese, Pascal Fries, Wolf Singer, View ORCID ProfileMartin Vinck
doi: https://doi.org/10.1101/2020.08.10.242958
Cem Uran
aErnst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany
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Alina Peter
aErnst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany
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  • ORCID record for Alina Peter
Andreea Lazar
aErnst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany
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William Barnes
aErnst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany
bMax Planck Institute for Brain Research, Frankfurt, Germany
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Johanna Klon-Lipok
aErnst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany
bMax Planck Institute for Brain Research, Frankfurt, Germany
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Katharine A Shapcott
aErnst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany
cFrankfurt Institute for Advanced Studies, Frankfurt, Germany
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Rasmus Roese
aErnst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany
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Pascal Fries
aErnst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany
dDonders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
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Wolf Singer
aErnst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany
bMax Planck Institute for Brain Research, Frankfurt, Germany
cFrankfurt Institute for Advanced Studies, Frankfurt, Germany
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Martin Vinck
aErnst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany
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  • ORCID record for Martin Vinck
  • For correspondence: martin.vinck@esi-frankfurt.de
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Abstract

Feedforward deep neural networks for object recognition are a promising model of visual processing and can accurately predict firing-rate responses along the ventral stream. Yet, these networks have limitations as models of various aspects of cortical processing related to recurrent connectivity, including neuronal synchronization and the integration of sensory inputs with spatio-temporal context. We trained self-supervised, generative neural networks to predict small regions of natural images based on the spatial context (i.e. inpainting). Using these network predictions, we determined the spatial predictability of visual inputs into (macaque) V1 receptive fields (RFs), and distinguished low- from high-level predictability. Spatial predictability strongly modulated V1 activity, with distinct effects on firing rates and synchronization in gamma-(30-80Hz) and beta-bands (18-30Hz). Furthermore, firing rates, but not synchronization, were accurately predicted by a deep neural network for object recognition. Neural networks trained to specifically predict V1 gamma-band synchronization developed large, grating-like RFs in the deepest layer. These findings suggest complementary roles for firing rates and synchronization in self-supervised learning of natural-image statistics.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵e martin.vinck{at}esi-frankfurt.de

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted August 10, 2020.
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Predictability in natural images determines V1 firing rates and synchronization: A deep neural network approach
Cem Uran, Alina Peter, Andreea Lazar, William Barnes, Johanna Klon-Lipok, Katharine A Shapcott, Rasmus Roese, Pascal Fries, Wolf Singer, Martin Vinck
bioRxiv 2020.08.10.242958; doi: https://doi.org/10.1101/2020.08.10.242958
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Predictability in natural images determines V1 firing rates and synchronization: A deep neural network approach
Cem Uran, Alina Peter, Andreea Lazar, William Barnes, Johanna Klon-Lipok, Katharine A Shapcott, Rasmus Roese, Pascal Fries, Wolf Singer, Martin Vinck
bioRxiv 2020.08.10.242958; doi: https://doi.org/10.1101/2020.08.10.242958

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