Hebbian Learning Meets Deep Convolutional Neural Networks

@inproceedings{Amato2019HebbianLM,
  title={Hebbian Learning Meets Deep Convolutional Neural Networks},
  author={Giuseppe Amato and Fabio Carrara and Fabrizio Falchi and Claudio Gennaro and Gabriele Lagani},
  booktitle={ICIAP},
  year={2019}
}
Neural networks are said to be biologically inspired since they mimic the behavior of real neurons. However, several processes in state-of-the-art neural networks, including Deep Convolutional Neural Networks (DCNN), are far from the ones found in animal brains. One relevant difference is the training process. In state-of-the-art artificial neural networks, the training process is based on backpropagation and Stochastic Gradient Descent (SGD) optimization. However, studies in neuroscience… CONTINUE READING

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Publications referenced by this paper.
SHOWING 1-10 OF 13 REFERENCES

Hebbian learning algorithms for training convolutional neural networks

Gabriele Lagani
  • Master’s thesis, School of Engineering,
  • 2019
VIEW 1 EXCERPT

Hebbian learning algorithms for training convolutional neural networks - project

Gabriele Lagani
  • code. https://github.com/GabrieleLagani/HebbianLearningThesis,
  • 2019
VIEW 1 EXCERPT