ArticleNature communications2023
Meta-learning biologically plausible plasticity rules with random feedback pathways.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Deep learning without weight symmetry.PNAS nexus · 2026Article
- Manifold-constrained plasticity enables stable learning in recurrent neural circuits.PLoS computational biology · 2026Article
- Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks.PLoS computational biology · 2025Article
- Flexible inference for animal learning rules using neural networks.Advances in neural information processing systems · 2025Article
- Neuroscientific insights about computer vision models: a concise review.Biological cybernetics · 2024Review
- Discovering plasticity rules that organize and maintain neural circuits.bioRxiv : the preprint server for biology · 2024Article
- Deep Learning without Weight Symmetry.ArXiv · 2024Article
- Specific connectivity optimizes learning in thalamocortical loops.Cell reports · 2024Article
- Meta-learning biologically plausible plasticity rules with random feedback pathways.Nature communications · 2023Article
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2 authors.
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Abstract
Backpropagation is widely used to train artificial neural networks, but its relationship to synaptic plasticity in the brain is unknown. Some biological models of backpropagation rely on feedback projections that are symmetric with feedforward connections, but experiments do not corroborate the existence of such symmetric backward connectivity. Random feedback alignment offers an alternative model in which errors are propagated backward through fixed, random backward connections. This approach successfully trains shallow models, but learns slowly and does not perform well with deeper models or online learning. In this study, we develop a meta-learning approach to discover interpretable, biologically plausible plasticity rules that improve online learning performance with fixed random feedback connections. The resulting plasticity rules show improved online training of deep models in the low data regime. Our results highlight the potential of meta-learning to discover effective, interpretable learning rules satisfying biological constraints.
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