Comparing the performance of Hebbian against backpropagation learning using convolutional neural networks
Author:
Funder
H2020 project AI4EU
H2020 project AI4Media
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-021-06701-4.pdf
Reference39 articles.
1. Amato G, Carrara F, Falchi F, Gennaro C, Lagani G (2019) Hebbian learning meets deep convolutional neural networks. In: International conference on image analysis and processing. Springer, pp 324–334
2. Bahroun Y, Soltoggio A (2017) Online representation learning with single and multi-layer hebbian networks for image classification. In: International conference on artificial neural networks. Springer, pp 354–363
3. Becker S, Plumbley M (1996) Unsupervised neural network learning procedures for feature extraction and classification. Appl Intell 6(3):185–203
4. Diehl PU, Cook M (2015) Unsupervised learning of digit recognition using spike-timing-dependent plasticity. Front Comput Neurosci 9:99
5. Ferré P, Mamalet F, Thorpe SJ (2018) Unsupervised feature learning with winner-takes-all based stdp. Front Comput Neurosci 12:24
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