Deep Feedback Control

Implementation of Deep Feedback Control (DFC) and Strong-DFC. The basis of this repository is a cleaned-up version of this public repository.

Requirements

The necessary conda environment to run the code is provided in the file dfc_environment.yml. To generate the environment type conda env create -f dfc_environment.yml and activate it with conda activate dfc_environment.

Note that you'll also need to install the hypnettorch library by doing python3 -m pip install hypnettorch.

Documentation

How to open the documentation is explained in the docs folder.

Running experiments

For running experiments, move to the dfc subfolder. Further instructions can be found on the README there. Command line arguments with good hyperparameter settings for the different DFC variants can be found in the EXPERIMENTS.rst file.

Citation

When using this package in your research project, please consider citing one of our papers for which this package has been developed.

@inproceedings{Meulemans2021Dec,
   title={Credit Assignment in Neural Networks through Deep Feedback Control},
   author={Alexander Meulemans and Matilde Tristany Farinha and Javier Garcia Ordonez and Pau Vilimelis Aceituno and Joao Sacramento and Benjamin F. Grewe},
   booktitle={Advances in Neural Information Processing Systems},
   year={2021},
   url={https://arxiv.org/abs/2106.07887}
}

@misc{https://doi.org/10.48550/arxiv.2204.07249,
  title = {Minimizing Control for Credit Assignment with Strong Feedback},
  author = {Meulemans, Alexander and Farinha, Matilde Tristany and Cervera, Maria R. and Sacramento, João and Grewe, Benjamin F.},
  publisher = {arXiv},
  year = {2022},
  url = {https://arxiv.org/abs/2204.07249},
}