Neural pathways and computations that achieve stable contrast processing tuned to natural scenes

<h1>Gur et al. 2024 database</h1> <p>Source data of the paper G&uuml;r et al. 2024, &ldquo;Neural pathways and computations that achieve stable contrast processing tuned to natural scenes&rdquo;, Nature Communications. This work contains an analysis of post-receptor luminance gain in the Drosophila visual system, focusing on the circuitry and algorithms for implementation of rapid luminance gain control.</p> <p>All data can be analyzed using the code provided in the Github repository: <a href="https://github.com/silieslab/Gur-etal-2024">https://github.com/silieslab/Gur-etal-2024</a>. Please go to the &ldquo;Readme&rdquo; file in the repository for how to use the code.</p> <h2>Raw data</h2> <p>All raw data is located in the folder &ldquo;raw_data&rdquo;. "Readme" file located in the code repository will guide you on how to analyze all data.</p> <h2>Processed data</h2> <p>All processed data is located in the folder &ldquo;processed_data&rdquo;. "Readme" file located in the code repository will guide you on how to analyze all data.</p> <p>- 2p_imaging_python_pickle: Processed data stored as .pickle files.&nbsp;<br>- Dm12_Figure7_Mat_files: Processed data for Dm12 imaging and optogenetics experiments presented in Figure 7 stored as .mat files.<br>- EM_data: Processed data for EM analysis done in Figure 7.<br>- Figure 6 Tm9 flpSTOP: tdTomato expression data for Figure 6 Tm9 flpSTOP experiments.<br>- Figure S5 Tm1 flpSTOP: tdTomato expression data for FigureS5 Tm1 flpSTOP experiments.</p>