The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Random synaptic feedback weights support error backpropagation for deep learning
Open accessAbstract
The brain processes information through multiple layers of neurons. This deep architecture is representationally powerful, but complicates learning because it is difficult to identify the responsible neurons when a mistake is made. In machine learning, the backpropagation algorithm assigns blame by multiplying error signals with all the synaptic weights on each neuron’s axon and further downstream. However, this involves a precise, symmetric backward connectivity pattern, which is thought to be impossible in the brain. Here we demonstrate that this strong architectural constraint is not required for effective error propagation. We present a surprisingly simple mechanism that assigns blame by multiplying errors by even random synaptic weights. This mechanism can transmit teaching signals across multiple layers of neurons and performs as effectively as backpropagation on a variety of tasks. Our results help reopen questions about how the brain could use error signals and dispel long-held assumptions about algorithmic constraints on learning.
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A deep learning framework for neuroscience
Richards, B. A., Lillicrap, T. P., Beaudoin, P., Bengio, Y., Bogacz, R., Christensen, A., Clopath, C., Costa, R. P., de Berker, A., Ganguli, S., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0520-2 · Cited by 1,027
Self-citationLillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J., & Hinton, G. (2020) · Nature Reviews Neuroscience · doi:10.1038/s41583-020-0277-3 · Cited by 908
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Metadata
- Authors
- Lillicrap, T. P., Cownden, D., Tweed, D. B., & Akerman, C. J.
- Venue
- Nature Communications
- Published
- 2016-11-08
- Volume
- 7
- Issue
- 1
- Pages
- 13276-13276
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2552737632
- Cited by
- 812
- References
- 22
- FWCI
- 54.22
- Type
- journal article
- Language
- en
- Open access
- gold
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