BrainProp

Pozzi, Isabella

Training deep networks with a biologically plausible algorithm. Implementation of BrainProp, a biologically plausible learning rule that can train deep neural networks on image-classification tasks (MNIST, CIFAR10, CIFAR100, Tiny ImageNet).

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Rights	MIT license
Organisation	Machine Learning
Citation APA Style AAA Style APA Style Cell Style Chicago Style Harvard Style IEEE Style MLA Style Nature Style Vancouver Style American-Institute-of-Physics Style Council-of-Science-Editors Style BibTex Format Endnote Format RIS Format CSL Format DOIs only Format	Pozzi, I. (2020). BrainProp.

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inProceedings Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagation I. Pozzi (Isabella), S.M. Bohte (Sander) and P.R. Roelfsema (Pieter)

BrainProp

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inProceedings
Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagation

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Publishing at CWI

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BrainProp

Publication

Publication

inProceedings Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagation

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inProceedings
Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagation