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Data and models underlying the analyses published in Höfling et al. (2024), eLife

Larissa Hoefling 585cadf78c Merge remote-tracking branch 'refs/remotes/public/master' 1 month ago
data ad9df4577a gin commit from lhoefling_GPU01-latest_gpu4 1 month ago
models f112cb4cbf git-annex in @lhoefling_GPU01-latest_gpu4 4 months ago
LICENSE 1b99976c40 Update 'LICENSE' 4 months ago
README.md bb92e96f3f Update 'README.md' 2 months ago

README.md

rgc-natstim

This repository contains the data and models underlying the analyses published in Hoefling et al. 2024: A chromatic feature detector signals visual context changes. The code to run the analyses can be found here.

Repository structure

The repository contains two sub-directories holding the data and models, respectively, which are structured as outlined below.

Models

The models directory is further sub-divided into a directory hodling the LN and CNN models, respectively, which each hold folders for individual models identified by hashes.

models/{linear, nonlinear}/{model_hashes}

Data

The data directory is sub-divided into four directories:

  • base: holds the basic preprocessed data as .hf files
  • movie: holds the movie stimulus files
  • mei: holds intermediate analysis results (Figs. 3-5 in the paper)
  • response_gradient: holds response gradient analysis results (Fig. 6 in the paper)

How to use this data

Clone* the contents of this repository to your machine using the gin CLI. Then follow instructions in the GitHub repository to re-run analyses or train models.

*You may have to explicitly download the contents of larger files using the gin get-content command. An UnpicklingError when trying to open a pickle file is usually resolved by downloading the contents of the file.

Authors and acknowledgements

See acknowledgments section in Hoefling et al. 2024: A chromatic feature detector signals visual context changes.