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Predictive coding of natural images by V1 activity revealed by self-supervised deep neural networks

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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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
eDonders Centre for Neuroscience, Department of Neuroinformatics, Radboud University Nijmegen, Nijmegen, Netherlands
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Abstract

Predictive coding is an important candidate theory of self-supervised learning in the brain. Its central idea is that neural activity results from an integration and comparison of bottom-up inputs with contextual predictions, a process in which firing rates and synchronization may play distinct roles. Here, we quantified stimulus predictability for natural images based on self-supervised, generative neural networks. When the precise pixel structure of a stimulus falling into the V1 receptive field (RF) was predicted by the spatial context, V1 exhibited characteristic γ-synchronization (30-80Hz), despite no detectable modulation of firing rates. In contrast to γ, β-synchronization emerged exclusively for unpredictable stimuli. Natural images with high structural predictability were characterized by high compressibility and low dimensionality. Yet, perceptual similarity was mainly determined by higher-level features of natural stimuli, not by the precise pixel structure. When higher-level features of the stimulus in the receptive field were predicted by the context, neurons showed a strong reduction in firing rates and an increase in surround suppression that was dissociated from synchronization patterns. These findings reveal distinct roles of synchronization and firing rates in the predictive coding of natural images.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

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

  • Figure 4, 7, 8 new

    Figure 1 modified

    Figure 3 new panels

    Figure 5 new panels

    Extended Data Figures 2, 4, 7–8, 12–18, 20–21 new

    Old Figure 6 removed (will be published in separate paper).

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 April 22, 2021.
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Predictive coding of natural images by V1 activity revealed by self-supervised deep neural networks
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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Predictive coding of natural images by V1 activity revealed by self-supervised deep neural networks
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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