Predictive coding of natural images by V1 firing rates and rhythmic 
synchronization

Uran, Cem; Peter, Alina; Lazar, Andreea; Barnes, William; Klon-Lipok, Johanna; Shapcott, Katharine A.; Roese, Rasmus; Fries, Pascal; Singer, Wolf; Vinck, Martin

doi:10.1016/j.neuron.2022.01.002

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Predictive coding of natural images by V1 firing rates and rhythmic synchronization

MPS-Authors

Uran, Cem
Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society;

Peter, Alina
Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society;

Lazar, Andreea
Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society;

Barnes, William
Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society;

Klon-Lipok, Johanna
Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society;

Shapcott, Katharine A.
Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society;

Roese, Rasmus
Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society;

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Fries, Pascal
Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society;
Fries Lab, Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society;

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Singer, Wolf
Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society;
Singer Lab, Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society;

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Vinck, Martin
Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society;
Vinck Lab, Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Max Planck Society;

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https://www.sciencedirect.com/science/article/pii/S0896627322000022
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Uran_2022_PredictiveCoding.pdf
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Citation

Uran, C., Peter, A., Lazar, A., Barnes, W., Klon-Lipok, J., Shapcott, K. A., et al. (2022). Predictive coding of natural images by V1 firing rates and rhythmic synchronization. Neuron, 110(7), 1240-1257.e8. doi:10.1016/j.neuron.2022.01.002.

Cite as: https://hdl.handle.net/21.11116/0000-000C-858A-2

Abstract

Predictive coding is an important candidate theory of self-supervised learning in the brain. Its central idea is that sensory responses result from comparisons between bottom-up inputs and contextual predictions, a process in which rates and synchronization may play distinct roles. We recorded from awake macaque V1 and developed a technique to quantify stimulus predictability for natural images based on self-supervised, generative neural networks. We find that neuronal firing rates were mainly modulated by the contextual predictability of higher-order image features, which correlated strongly with human perceptual similarity judgments. By contrast, V1 gamma (γ)-synchronization increased monotonically with the contextual predictability of low-level image features and emerged exclusively for larger stimuli. Consequently, γ-synchronization was induced by natural images that are highly compressible and low-dimensional. Natural stimuli with low predictability induced prominent, late-onset beta (β)-synchronization, likely reflecting cortical feedback. Our findings reveal distinct roles of synchronization and firing rates in the predictive coding of natural images.