Code repository: Signatures of hierarchical temporal processing in the mouse visual system

This repository contains the analysis pipeline and plotting routines for our preprint

Arxiv

@misc{rudelt_signatures_2023,
  doi = {},
  url = {},
}

Refactoring Note

We are currently cleaning this repository.

This requires converting all scripts to a new data backend, using our prepared spiking data in a minimal format (to avoid AllenSDK dependencies to just reproduce the paper).

The current state is as follows:

• branching_network/ BN simulation results are independent, self-contained and up to date.
• experiment_analysis/ contains updated scripts.
• experiment_legacy/ contains the old scripts, which are not compatible with the new data format, and instead the AllenSDK.
• we provide the new data format and the intermediate analysis results in the old format (for legacy plot scripts), in the data repository on gin.g-node.org.

Data

We analyse data of mouse visual cortex from the Allen Brain Atlas. The data is accessed using the Allen SDK. For convenience, we provide a copy of the preprocessed data that is compatible with our analysis pipeline on gin.g-node.org. Loading these files requires only minimal dependencies and should be easy to setup using our environment.yaml. All use of these data must comply with the orignal sources' Terms of Use.

Instructions on how to download the data can be found in the docs. In the folder experiment_analysis/data we provide scripts that download the spike data and related metrics

• download_csv_files.py downloads csv files containing information regarding the experimental sessions, probes, channels and sorted unit, as well as other analysis metrics such as stimulus selectivity. This will create a file brain_observatory_unit_metrics_filtered.csv, which is required for further analysis regarding stimulus selectivity.
• download_session_data.py downloads the full session data containing spike data for each experimental session of both the functional_connectivity and brain_observatory_1_1 experiments
• if this does not work well, you can also execute download_session_data_via_http.py to download the data directly using http (see docs for more info)

TODO: Add info about write_spike_times.py to create the h5 files from the raw spike data.

Plotting

• Figure 1, legacy code

  • allen_single_units.py
  • allen_single_units_stimulus.py

• Figure 2, legacy code

  • require processed data using old pipeline, or the results from stats.zip
  • allen_grouped.py
  • allen_hierarchy.py
  • allen_hierarchical_bayes_model_comparison.py creates hdf5 files, to plot with
  • allen_bayes.ipynb

• Figure 3, up to date

  • branching_network/notebooks/bn_plot_measures_vs_a.ipynb
  • branching_network/notebooks/bn_raster_examples.ipynb

• Figure 4, legacy code

  • measures_vs_allen_metrics_scatter.py

• Figure S7, up to date

  • Illustrates the autocorrelation fits and the new dataformat.
  • experiment_analysis/notebooks/single_unit_autocorrelation.ipynb

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Under construction, not yet up to date:

Analysis

All analysis run on our preprocessed data and all requirements can be installed by creating a new conda environment

conda env create -f environment.yaml --name mouse_visual_timescales