This repository is associated with the paper: Shallow neural networks trained to detect collisions recover features of visual loom-selective neurons (Link), where we built neural network models to solve an inference problem: whether or not the incoming visual signals indicate an object on a collision course. The models trained from this inference problem exhibit a large range of properties, including structures, response curves and tunings, that resemble the loom-sensitive LPLC2 neurons in the fly brain.

The folders are organized as follows:

stimulus_core: core files to generate stimuli for training and testing.

stimulus_generation: files to perform the stimuli generation processes.

models_core: core files of the models and trainings.

models_training: files to perform the training and testing processes.

analysis: files and notebooks to perform data/model analysis and figure generations.

helper: files that contain all the helper functions.

results: main results, figures and videos are saved here.

env: environment.

How to use

0. The environment can be set up using py37_dev.yml in the folder env.

1. Need at least 300 GB of space to save the stimuli and training results. The whole process takes days or even weeks to run depending on your computing power.

2. The letter M indicates the number of units in the model, and in our paper, it takes values 1, 2, 4, 8, 16, 32, 64, 128, 192, 256.

3. Use samples_generation_multi_units_run_smallM.py and samples_generation_multi_units_run_largeM.py in the folder stimulus_generation to generate the stimuli for training and testing. You should use more than 30 cores to do this.

4. Use train_multiple_units_M{M}.py in the folder models_training to train and test the model, where {M} indicates 1, 2, 4, 8, 16, 32, 64, 128, 192, 256. You should use more than 30 cores to do this.

5. Use model_clustering.ipynb in analysis to do the model clustering. You need to run the whole notebook for very M, and so that you can check the clustering by eyes through some figures generated. This sets the foundation for Figures 5-10.

6. Use get_samples_for_movie_single_unit.py and get_samples_for_movie_multi_units.py in analysis to generate high resolution samples for plotting of both movies and figures. This is for Figure 3, Figure 8 and its supplementations, and videos.

7. Use movie_3d.py and movie_3d.ipynb in analysis to generate 3d rendering samples. This is for Figure 3, and videos.

8. Use calculate_response_vs_distance_vs_angles.py in analysis to calculate the responses of the models to different types of stimuli. This is for Figure 7.

9. Use calculate_incoming_angles.py in analysis to calculate the incoming angles of hit stimuli. This is for Figure 7.

10. Use samples_generation_grid.py in stimulus_generation and get_grid_response.py in analysis to generate grid samples and grid responses. This is for Figure 7.

11. Use sparse_dense_response.py in analysis to calculate active units in models. This is for Figure 8.

12. Use get_probability_of_hit.py in analysis to calculate probability of hit. This is for Figure 8.

13. Use get_Klapoetke_stimuli.py in stimulus_core, samples_generation_linear_law.py in stimulus_generation and replication.py in analysis to reproduce experimental data. All experimental data are from Card's lab. This is for Figure 10.

14. Use tuning_curve.py in stimulus_generation to generate data for HRC tuning curves. This is for supplementation figure of Figure 3.

15. Use get_HRC_output_all.py in analysis to collect all the HRC outputs for different data types, respectively. This is for supplementation figure of Figure 3.

16. Use plot_all_movies.ipynb in analysis to get the movies.

17. Use plot_all_main_figures.ipynb in analysis to get the figures.

Contributions

Stimuli generation, figures and videos: Baohua Zhou

Model training: Zifan Li, Baohua Zhou