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

Gur et al. 2024 database

Source data of the paper Gür et al. 2024, “Neural pathways and computations that achieve stable contrast processing tuned to natural scenes”, 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.

All data can be analyzed using the code provided in the Github repository: https://github.com/silieslab/Gur-etal-2024. Please go to the “Readme” file in the repository for how to use the code.

Raw data

All raw data is located in the folder “raw_data”. "Readme" file located in the code repository will guide you on how to analyze all data.

Processed data

All processed data is located in the folder “processed_data”. "Readme" file located in the code repository will guide you on how to analyze all data.

- 2p_imaging_python_pickle: Processed data stored as .pickle files. - Dm12_Figure7_Mat_files: Processed data for Dm12 imaging and optogenetics experiments presented in Figure 7 stored as .mat files.- EM_data: Processed data for EM analysis done in Figure 7.- Figure 6 Tm9 flpSTOP: tdTomato expression data for Figure 6 Tm9 flpSTOP experiments.- Figure S5 Tm1 flpSTOP: tdTomato expression data for FigureS5 Tm1 flpSTOP experiments.