Here we provide the complete data for the article Franke, Willeke et al. Nature 2022 'State-dependent pupil dilation rapidly shifts visual feature representations'.
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.
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.
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.
For further information about the datasets, analysis code, and access to the public database of the modelling results, see: https://github.com/sinzlab/nndichromacy
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).
