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

Uran, C.; Peter, A.; Lazar, A.; Barnes, W.; Klon-Lipok, J.; Shapcott, K. A.; Roese, R.; Fries, P.; Singer, W.; Vinck, M.

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

Uran, C., Peter, A., Lazar, A., Barnes, W., Klon-Lipok, J., Shapcott, K. A., et al. (2020). Predictability in natural images determines V1 firing rates and synchronization: A deep neural network approach. bioRxiv.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0007-81CE-F Version Permalink: https://hdl.handle.net/21.11116/0000-0007-81D4-7

Genre: Journal Article

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Creators:
Uran, C., Author
Peter, A., Author
Lazar, A.¹, Author
Barnes, W.¹, Author
Klon-Lipok, J.¹, Author
Shapcott, K. A.¹, Author
Roese, R., Author
Fries, P., Author
Singer, W.¹, Author
Vinck, M., Author

Affiliations:
1Neurophysiology Department, Max Planck Institute for Brain Research, Max Planck Society, Max-von-Laue-Str. 4, 60438 Frankfurt am Main, DE, ou_2461705

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Dates: Published Online: 2020-08-10

Publication Status: Published online

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Title: bioRxiv

Source Genre: Journal

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Pages: 1-15 Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: Other: https://doi.org/10.1101/2020.08.10.242958