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2016

Learning to learn with backpropagation of Hebbian plasticity

Miconi, Thomas

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Understand

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 plastic connections.

Learning to learn with backpropagation of Hebbian plasticity · Around

Built on

“Review of stability properties of neural plasticity rules for implementation on memristive neuromorphic hardware”
Zlatko Vasilkoski et al · 2011
Earlier work this paper cites.
“Development and plasticity of the primary visual cortex”
J˜Sebastian Espinosa and Michael˜P Stryker · 2012
Earlier work this paper cites.

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