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Deep Learning With Asymmetric Connections and Hebbian Updates
We show that deep networks can be trained using Hebbian updates yielding similar performance to ordinary back-propagation on challenging image datasets. To overcome the unrealistic symmetry in connections between layers, implicit in back-propagation, the feedback weights are separate from the feedfo...
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| Veröffentlicht in: | Front Comput Neurosci |
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| 1. Verfasser: | |
| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
Frontiers Media S.A.
2019
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| Schlagworte: | |
| Online Zugang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6458299/ https://ncbi.nlm.nih.gov/pubmed/31019458 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/fncom.2019.00018 |
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