ArticleProceedings of the National Academy of Sciences of the United States of America2023
NMDA-driven dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Top-down feedback in deep neural networks leads to functional differences during audiovisual integration.eLife · 2026Article
- Flexible integration of natural stimuli by auditory cortical neurons.Journal of neurophysiology · 2026Article
- Going deeper with morphologically detailed neural networks by simulation-based gradient propagation.Frontiers in computational neuroscience · 2026Article
- Learning to combine top-down context and feed-forward representations under ambiguity with apical and basal dendrites.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Article
- Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning.Nature communications · 2025Article
- Simplified two-compartment neuron with calcium dynamics capturing brain-state specific apical-amplification, -isolation and -drive.Frontiers in computational neuroscience · 2025Article
- Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning.ArXiv · 2024Article
- NMDA-driven dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Article
- Cellular computation and cognition.Frontiers in computational neuroscience · 2023Article
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Authors and funding
7 authors.
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Abstract
While sensory representations in the brain depend on context, it remains unclear how such modulations are implemented at the biophysical level, and how processing layers further in the hierarchy can extract useful features for each possible contextual state. Here, we demonstrate that dendritic N-Methyl-D-Aspartate spikes can, within physiological constraints, implement contextual modulation of feedforward processing. Such neuron-specific modulations exploit prior knowledge, encoded in stable feedforward weights, to achieve transfer learning across contexts. In a network of biophysically realistic neuron models with context-independent feedforward weights, we show that modulatory inputs to dendritic branches can solve linearly nonseparable learning problems with a Hebbian, error-modulated learning rule. We also demonstrate that local prediction of whether representations originate either from different inputs, or from different contextual modulations of the same input, results in representation learning of hierarchical feedforward weights across processing layers that accommodate a multitude of contexts.
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