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Learning to learn with backpropagation of Hebbian plasticity
[article]
2016
arXiv
pre-print
Hebbian plasticity is a powerful principle that allows biological brains to learn from their lifetime experience. By contrast, artificial neural networks trained with backpropagation generally have fixed connection weights that do not change once training is complete. While recent methods can endow neural networks with long-term memories, Hebbian plasticity is currently not amenable to gradient descent. Here we derive analytical expressions for activity gradients in neural networks with Hebbian
arXiv:1609.02228v2
fatcat:puyhnd7f3rdxxb7kecrjjznmn4