Repository for the code used in generating, training and evaluating temperature navigation ANNs in (Haesemeyer, Schier & Engert,Neuron, 2019).
Includes generally required classes and functions including classes to set up, save and load all ANN models.
Contains routines for the analysis of behavioral and neural data.
Contain "species specific" implementations of arena simulations and white noise simulations as well as simulations for training data generation for predictive models. Simulator for reinforcement learning models is contained in the RL_trainingGround.py file since this model learns during the simulation.
Contain code for training various predictive and reinforcement learning models.
Interface classes that allow storing the runs of ANN simulations since these take a considerable amount of time.
The following files were generated for testing purposes only and have not been used in the actual publication:
- activationMovie.py
- analyzeTempResponses.py
- ce_retrain.py
- gradientSimulation.py
- mixedInputModel.py
- plot_data.py
- singleInputModel.py
While spectral clustering will essentially draw the same cluster boundaries on every invocation, the assignment of units to individual cluster indices is not deterministic (in other words, the activity profile belonging to cluster 1 on one invocation may be found in cluster 5 on the next). Therefore any correspondence between zebrafish / C. elegans types and ANN cluster centroids has to be adjusted accordingly. This includes retraining and ablations.
For zebrafish the assignments are found by calling zfish_ann_correspondence.py whereas they are visual in the case of C. elegans, in other words here the ANN clusters are assigned based on the closest match in the literature.
Since Figure panels were moved around during the publication process, the assignment of panels to Figures in the Figure*.py scripts is only approximate. Also, all plots related to reinforcement learning networks are in rl_plots.py irrespective of their paper figure location.
The code depends on the libraries listed below. Note that version information merely refers to the version that was used and likely does not indicate a strict requirement for that particular library version.
- python - 3.6
- tensorflow - 1.10
- numpy - 1.15
- scipy - 1.1
- scikit-learn - 0.19
- matplotlib - 3.0
- seaborn - 0.9
- pandas - 0.23
- h5py - 2.8
Associated project data are deposited on zenodo.org:
All code is licensed under the MIT license. See LICENSE for details.
© Martin Haesemeyer, 2017-2019