This directory contains:


- `analysis_code.tar/`: Contains the code used to analyse the physiological data.
   All code was created and tested with Python version 3.10.8.
   The following libraries were imported:
      matplotlib version 3.6.2
      numpy version 1.23.4
      pandas version 1.5.2
      Pytorch version 1.13.0
      scipy version 1.7.1
	
    - 'plots.py': Functions to create Figures 3A,B, 8, and Supplementary Figures 1A,B and 2A,B from the original manuscript.
    
    - 'posteriors.csv': Posteriors for all analysed neurons. This file contains results computed in 'score_computations.py'.
    
    - 'requirements.txt' : The required python packages, as listed above.
	
    - 'score_computations.py': Compute firing posteriors and (absolute) motion sensitivity and direction selectivity scores. Execution creates Figures 3A,B and Supplementary Figures 1A,B and 2A,B.
    
    - 'scores.csv': (Absolute) motion sensitivity and direction selectivity scores for all analysed neurons. This file contains results computed in 'score_computations.py'.
	
    - 'threeway_test.py': Functions for computations in score_computations.py. Execution creates Figure 8 from the appendix of the original manuscript.

	
- `data.tar/`: Meta and physiological data. Consult the `README.md` file in this folder for further details.

    - /csv_data
	   - <neuron id>_<direction code>.csv
             Each file is named according to the following convention:
	     - neuron id is the unique ID assigned to each neuron.
	     - direction code encodes the direction of visual motion presented during the motion phase (b=backward, f=forward, lt=left turn, rt=right turn, lr=left roll, rr=right roll, u=up, d=down).
             Each file comprises the following data:
	     - nSpikes1: Number of spikes in the motion condition
	     - nBins1:   Number of 2 ms time bins in the motion condition
	     - nSpikes2: Number of spikes in the baseline condition
	     - nBins2:   Number of 2 ms time bins in the baseline condition
		
    - `fz2_metadata.csv`: Meta data for each neuron with recorded data in csv_data/:
           - The unique ID assigned to each recorded neuron ('id')
           - The assigned neuron type ('Neuron type', Abbreviations as employed in the manuscript)
           - The brain hemisphere in which its soma is located ('Hemisphere_soma', 'l' for left, 'r' for right, and an empty cell if it could not be determined unambiguously)

- `model_code.tar/`: Contains the code for the Computational Model.
   All code was created and tested with Python version 3.10.8.
   The following libraries were imported:
      matplotlib version 3.6.2
      numpy version 1.23.4
      Pytorch version 1.13.0

    - 'create_matrix.py': Function to create a network connectivity matrix.

    - 'plots.py': Functions to create Figures 6 and 7 as well as plots of indermediary results for 'train_models.py'.
    
    - 'requirements.txt' : The required python packages, as listed above.
	
    - 'simulation.py': Agent simulation shown in Figure 7.
	
    - 'train_models.py': Optimize models for compass state maintenance and rotation-induced shifts.
	
    - 'utils.py': Utility functions used in other scripts.
    
	
- 'Manuscript.pdf': Pre-Print of the Manuscript, including Supplementary Materials.


To execute the analyses, the archives data.tar and analysis_code.tar must be unpacked into the same folder, preserving their internal folder structure.
To execute the model optimization and simulation, model_code.tar can be unpacked independently of analyses_code.tar and data.tar.