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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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-04-01
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| Series: | Frontiers in Computational Neuroscience |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fncom.2019.00018/full |