ArticleFrontiers in neuroscience2023
Context association in pyramidal neurons through local synaptic plasticity in apical dendrites.
Article in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Dendritic nonlinearities mitigate communication costs.Patterns (New York, N.Y.) · 2026Article
- Intrinsic stabilization of synaptic plasticity improves learning and robustness in artificial neural networks.Nature communications · 2026Article
- Learned adaptive properties for mitigation of weight perturbations in embedded spiking networks.Frontiers in neuroscience · 2026Article
- Glutamate, Contextual Insensitivity, and Disorganized Speech in First-Episode Schizophrenia: A 7T Magnetic Resonance Spectroscopy Study.Biological psychiatry global open science · 2025Article
- 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
- Advancing neural computation: experimental validation and optimization of dendritic learning in feedforward tree networks.American journal of neurodegenerative disease · 2024Article
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2 authors.
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Abstract
The unique characteristics of neocortical pyramidal neurons are thought to be crucial for many aspects of information processing and learning in the brain. Experimental data suggests that their segregation into two distinct compartments, the basal dendrites close to the soma and the apical dendrites branching out from the thick apical dendritic tuft, plays an essential role in cortical organization. A recent hypothesis states that layer 5 pyramidal cells associate top-down contextual information arriving at their apical tuft with features of the sensory input that predominantly arrives at their basal dendrites. It has however remained unclear whether such context association could be established by synaptic plasticity processes. In this work, we formalize the objective of such context association learning through a mathematical loss function and derive a plasticity rule for apical synapses that optimizes this loss. The resulting plasticity rule utilizes information that is available either locally at the synapse, through branch-local NMDA spikes, or through global Ca
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