CePNEM model analysis data and ANTSUN and microscopy neural network weights

Citation and Publication To cite this project (datasets, encoding data, methods, modeling, code, website, etc.), please refer to Atanas & Kim et al., 2023 below. Please cite the paper directly and DO NOT cite this repository. Brain-wide representations of behavior spanning multiple timescales and states in C. elegans Adam A. Atanas*, Jungsoo Kim*, Ziyu Wang, Eric Bueno, McCoy Becker, Di Kang, Jungyeon Park, Talya S. Kramer, Flossie K. Wan, Saba Baskoylu, Ugur Dag, Elpiniki Kalogeropoulou, Matthew A. Gomes, Cassi Estrem, Netta Cohen, Vikash K. Mansinghka, Steven W. Flavell * equal Contribution Links: https://doi.org/10.1016/j.cell.2023.07.035 https://pubmed.ncbi.nlm.nih.gov/37607537/ WormWideWeb Datasets in this project can be interactively visualized on: https://wormwideweb.org/ Features include: Find specific neurons Find specific datasets Contents Original files fit_results.jld2.bz2: model fit results fit_results_lite.jld2.bz2: summarized fit results umap_dict.jld2.bz2: UMAP projection data analysis_dict.jld2.bz2: analysis-related information dict_neuropal_label.jld2.bz2: neuropal label (raw/source data. this maps segmentation rois to labels) relative_encoding_strength.jld2.bz2: relative encoding strength posteriors deepnet-weights.tar.bz2: deep neural nets weights Summarized data files using generate_encoding_files() of WormWideWebData.jl (https://github.com/flavell-lab/WormWideWebData.jl) neuropal_label.jld2.bz2: matched to roi (use this for analysis) neuropal_label.json.bz2: neuropal labels fit_ranges.h5.bz2: model fit time segment ranges sampled_tau_vals_median.h5.bz2: tau (decay-constants) posterior medians relative_encoding_strength_median.h5.bz2: relative encoding strength posterior medians tuning_strength.h5.bz2: tuning strength info encoding_changes_corrected.h5.bz2: encoding changes info neuron_categorization.h5.bz2: encoding categorization info deepnet-weights.tar.bz2 contains the trained weights of the neural networks used in this project. 3dunet_540nm_voxels: 3D U-Net for segmenting neurons head_detector_unet: finding worm head landmark used in ANTSUN registration head_detector_unet_0622: an alternative version of the above, optimal for NeuroPAL datasets microscope_tracker: detecting keypoints for online tracking on the microscope behavior_nir: segmentation of the recorded NIR behavior images for behavior quantification Neural and behavioral datasets ANTSUN processed datasets and CePNEM processed model fits and analysis data. Check the project packages and notebooks in the project github repository (https://github.com/flavell-lab/AtanasKim-Cell2023/) on using these datasets. To get all behavior-neural datasets, please see processed_h5.tar.bz2 processed_h5_scrambled.tar.bz2: contains the scrambled datasets used for the control analysis. Notes list of all datasets: lists the datasets and their metadata and other info for head angle-related behaviors such as head angle and angular velocity, θh_pos_is_ventral needs to be used to correct the sign (e.g. dv_correction = θh_pos_is_ventral ? -1 : 1). θh_pos_is_ventral info for each dataset is available in the csv file above ("list of all datasets") History v1: original v2: added neural datasets file (processed_h5.tar.bz2) v3: added stage location information (processed_h5.tar.bz2) v4: adjusted stage location information keys for consistency. added encoding-related and neuropal files for WormWideWeb