This web site provides code and data files that implement the analyses performed in the following paper: > Kay, K., Prince, J., Gebhart, T., Zhou, J., Naselaris, T., Schutt, H. Disentangling signal and noise in neural responses through generative modeling. The pre-print is available at https://doi.org/10.1101/2024.04.22.590510 The code toolbox that implements the GSN (Generative signal and noise modeling) technique is available at https://github.com/cvnlab/GSN/. The code provided on this site has several dependencies: * https://github.com/cvnlab/knkutils/ * https://github.com/cvnlab/GSN/ Description of files: * The scripts \*Figure\*.m perform the analyses and make the figures in the paper. * The file Figure6andFigureS2.mat contains some data that serves as a convenient checkpoint for Figure6andFigureS2.m. * The files scncC*.mat are large data files that serve as a convenient checkpoint for Figure7andFigureS3andFigureS4.m. * The script preparensddata.m performs data preparation, ultimately producing the scncC*.mat files. * The files Faces_semiautomated_combined_1k.mat, nsd_expdesign.mat, samplegsnoutput.mat, and scenario*.mat are various data files that are loaded by the scripts. * The files fitloglogline.m and SIMULATIONS_helper.m are helper functions used by the scripts. * The file exampledata.mat is used in the code examples in the GSN code toolbox. * The file MEME.zip contains our implementation of the MEME method (based on code provided at https://github.com/dp4846/meme_v1_bpl). The results of our MEME implementation are loaded and used in the script Figure5andFigure4andFigureS1.m.