Data for the Franke, Willeke, et al. (2022) article: 'State-dependent pupil dilation rapidly shifts visual feature selectivity'

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README.md

Data for the Franke, Willeke, et al. (2022) article: 'State-dependent pupil dilation rapidly shifts visual feature selectivity'

Summary

Here we provide the complete data for the article Franke, Willeke, et al., 2022 'State-dependent pupil dilation rapidly shifts visual feature selectivity'. https://www.nature.com/articles/s41586-022-05270-3

The data consists of 50 individual datasets (i.e. recording scans) of calcium activity of L2/3 neurons in mouse V1. All datasets were acquired using two-photon imaging of awake, head-fixed mice.

Downloading the data

Using the web browser

When clicking the download button at the top right, the whole repository will be downloaded as a zipped file. However, large datafiles will be skipped and a placeholder is downloaded instead. To download the large data files in the sub-directories (either .h5 or .zip), download the files individually from the web interface by clicking on them in the repository browser.

Repository structure

The datasets are divided into sub-directories based on the experimental paradigm.

Imagenet scans contain the neuronal activity in response to colored naturalistic images. We used these scans for training deep convolutional neural networks to learn an in-silico model of the recorded neuronal population.

Dotmap scans: A sparse noise paradigm for mapping receptive fields of visual neurons. We used these scans to confirm the predictions from our in-silico analysis.

Decoding scans: A paradigm with artificial stimuli to test decoding discriminability and decoding detection performance of a population of V1 neurons.

An overview of the structure of each individual dataset can be found within the sub-directories.

Related Repositories

For further information about the datasets, analysis code, and access to the public database of the modelling results, see: https://github.com/sinzlab/nndichromacy

Licensing

Creative Commons License

This data is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. This license requires that you contact us before you use the data in your own research. In particular, this means that you have to ask for permission if you intend to publish a new analysis performed with this data (no derivative works-clause).

datacite.yml
Title State-dependent pupil dilation rapidly shifts visual feature representations
Authors Franke,Katrin;Institute for Ophthalmic Research, Tuebingen University, Tuebingen, Germany.
Willeke,Konstantin F.;Institute for Bioinformatics and Medical Informatics, Tuebingen University, Tuebingen, Germany
Ponder,Kayla;Department of Neuroscience, Baylor College of Medicine, Houston, TX, US
Galdamez,Mario;Department of Neuroscience, Baylor College of Medicine, Houston, TX, US
Zhou,Na;Department of Neuroscience, Baylor College of Medicine, Houston, TX, US
Muhammad,Taliah;Department of Neuroscience, Baylor College of Medicine, Houston, TX, US
Patel,Saumil;Department of Neuroscience, Baylor College of Medicine, Houston, TX, US
Froudarakis,Emmanouil;Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology Hellas, Heraklion, Greece
Reimer,Jacob;Department of Neuroscience, Baylor College of Medicine, Houston, TX, US
Sinz,Fabian;Department of Computer Science, Goettingen University, Goettingen, Germany
Tolias,Andreas;Department of Neuroscience, Baylor College of Medicine, Houston, TX, US
Description Complete dataset for the article "State-dependent pupil dilation rapidly shifts visual feature representations".
License CC-BY (http://creativecommons.org/licenses/by/4.0/)
References Franke, K., Willeke, K.F., Ponder, K., Galdamez, M., Muhammad, T., Patel, S., Froudarakis, E., Reimer, J., Sinz, F., & Tolias, A.S. (2021). Behavioral state tunes mouse vision to ethological features through pupil dilation. bioRxiv. [doi:10.1101/2021.09.03.458870] (IsSupplementTo)
Funding DFG, EXC 2064/1
IARPA, D16PC00003
NIH, R01 EY026927
NIH, T32-EY-002520-37
NSF, 1707400
Carl-Zeiss-Stiftung
Keywords Neuroscience
Systems Neuroscience
Primary visual cortex
Brain state
Behavioral state
Machine learning
Deep neural networks
Resource Type Dataset