Pytorch implementation of Hebbian learning algorithms to train deep convolutional neural networks. A neural network model is trained on CIFAR10 both using Hebbian algorithms and SGD in order to compare the results. Although Hebbian learning is unsupervised, I also implemented a technique to train the final linear classification layer using the Hebbian algorithm in a supervised manner. This is done by applying a teacher signal on the final layer that provides the desired output; the neurons are then enforced to update their weights in order to follow that signal.

You might want to give a look at the new repos as well!
HebbianPCA: https://github.com/GabrieleLagani/HebbianPCA/blob/master/README.md
Latest updates: https://github.com/GabrieleLagani/HebbianLearning

In order to launch a training session, type:
PYTHONPATH=<project root> python <project root>/train.py --config <config family>/<config name>
Where <config family> is either gdes or hebb, depending whether you want to run gradient descent or hebbian training, and <config name> is the name of one of the training configurations in the config.py file.
Example:
PYTHONPATH=<project root> python <project root>/train.py --config gdes/config_base
To evaluate the network on the CIFAR10 test set, type:
PYTHONPATH=<project root> python <project root>/evaluate.py --config <config family>/<config name>

For further details, please refer to my thesis work:
"Hebbian Learning Algorithms for Training Convolutional Neural Networks; G. Lagani"
available at https://etd.adm.unipi.it/theses/available/etd-03292019-220853/unrestricted/hebbian_learning_algorithms_for_training_convolutional_neural_networks_gabriele_lagani.pdf and the related paper:
"Hebbian Learning Meets Deep Convolutional Neural Networks; G. Amato, F. Carrara, F. Falchi, C. Gennaro and G. Lagani"
available at: http://www.nmis.isti.cnr.it/falchi/Draft/2019-ICIAP-HLMSD.pdf

Author: Gabriele Lagani - gabriele.lagani@gmail.com