A biologically plausible implementation of the HSIC objective. Please cite our work if you use this codebase for research purposes:

@article{10.3389/fncom.2024.1240348,
	author = {Daruwalla, Kyle and Lipasti, Mikko},
	doi = {10.3389/fncom.2024.1240348},
	issn = {1662-5188},
	journal = {Frontiers in Computational Neuroscience},
	title = {Information bottleneck-based Hebbian learning rule naturally ties working memory and synaptic updates},
	url = {https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2024.1240348},
	volume = {18},
	year = {2024},
	bdsk-url-1 = {https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2024.1240348},
	bdsk-url-2 = {https://doi.org/10.3389/fncom.2024.1240348}
}

To install the codebase, install Micromamba (or Conda), then run:

micromamba create -f environment.yaml
poetry install --no-root

The code for running the experiments is located in the root directory with auxiliary code located in projectlib. Configuration files use Hydra and are found in configs.

To run the reservoir experiment, activate the project environment, and run:

python train-reservoir.py

The results will be stored under outputs/train-reservoir.

To run the linear synthetic dataset experiment, activate the project environment, and run:

python train-lif-biohsic.py data=linear

The results will be stored under output/train-lif-biohsic.

To run the XOR synthetic dataset experiment, activate the project environment, and run:

python train-lif-biohsic.py data=xor

The results will be stored under output/train-lif-biohsic.

To run the MNIST experiment, activate the project environment, and run:

python train-biohsic.py data=mnist

The results will be stored under output/train-biohsic.

To run the CIFAR-10 experiment, activate the project environment, and run:

python train-biohsic.py data=cifar10 model=cnn

The results will be stored under output/train-biohsic.

The back-propagation baselines can be run using the same commands but with the train-bp.py script.

The plotting.ipynb can recreate the plots given the results stored in the output directories above. You may need to adjust the paths in the notebook to your results.