ArticleProceedings of the National Academy of Sciences of the United States of America2021
Local dendritic balance enables learning of efficient representations in networks of spiking neurons.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Desegregation of neuronal predictive processing.Nature communications · 2026Article
- Locality and weight sharing shape Hebbian principal-component learning in biologically constrained visual models.Frontiers in computational neuroscience · 2026Article
- Predictive learning rules generate a cortical-like replay of probabilistic sensory experiences.eLife · 2025Article
- Article
- Omission responses in local field potentials in rat auditory cortex.BMC biology · 2023Article
- Review
- Emergence of synaptic organization and computation in dendrites.Neuroforum · 2022Review
- Local dendritic balance enables learning of efficient representations in networks of spiking neurons.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
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3 authors.
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Abstract
How can neural networks learn to efficiently represent complex and high-dimensional inputs via local plasticity mechanisms? Classical models of representation learning assume that feedforward weights are learned via pairwise Hebbian-like plasticity. Here, we show that pairwise Hebbian-like plasticity works only under unrealistic requirements on neural dynamics and input statistics. To overcome these limitations, we derive from first principles a learning scheme based on voltage-dependent synaptic plasticity rules. Here, recurrent connections learn to locally balance feedforward input in individual dendritic compartments and thereby can modulate synaptic plasticity to learn efficient representations. We demonstrate in simulations that this learning scheme works robustly even for complex high-dimensional inputs and with inhibitory transmission delays, where Hebbian-like plasticity fails. Our results draw a direct connection between dendritic excitatory-inhibitory balance and voltage-dependent synaptic plasticity as observed in vivo and suggest that both are crucial for representation learning.
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