ArticleNeuron2021
A synaptic learning rule for exploiting nonlinear dendritic computation.
Article in Neuron, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed.
- Linking functional and structural dendritic spine remodeling during fear learning and extinction in vivo.Science advances · 2026Article
- Article
- What can a neuron compute.bioRxiv : the preprint server for biology · 2026Article
- Blood-catalyzed n-doped polymers for reversible optical neural control.Science (New York, N.Y.) · 2026Article
- Going deeper with morphologically detailed neural networks by simulation-based gradient propagation.Frontiers in computational neuroscience · 2026Article
- Optogenetic interrogation of the zebrafish lateral line reveals brain-wide neural circuits involved in pattern separation.iScience · 2025Article
- Transient boosting of action potential backpropagation for few-shot temporal pattern learning.PLoS computational biology · 2025Article
- Article
- Article
- Data-driven synapse classification reveals a logic of glutamate receptor diversity.bioRxiv : the preprint server for biology · 2025Article
- A method for quantifying parallel growth between neuronal dendritic branches in vitro.PloS one · 2025Article
- Learning to use landmarks for navigation amplifies their representation in retrosplenial cortex.bioRxiv : the preprint server for biology · 2024Article
- The plasticity of pyramidal neurons in the behaving brain.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Review
- Functional architecture of intracellular oscillations in hippocampal dendrites.Nature communications · 2024Article
- Probing multiplexed basal dendritic computations using two-photon 3D holographic uncaging.Cell reports · 2024Article
- Variable recruitment of distal tuft dendrites shapes new hippocampal place fields.bioRxiv : the preprint server for biology · 2024Article
- Functional architecture of intracellular oscillations in hippocampal dendrites.bioRxiv : the preprint server for biology · 2024Article
- Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics.Nature communications · 2024Article
- Brain-like hardware, do we need it?Frontiers in neuroscience · 2024Review
- A GPU-based computational framework that bridges neuron simulation and artificial intelligence.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Information processing in the brain depends on the integration of synaptic input distributed throughout neuronal dendrites. Dendritic integration is a hierarchical process, proposed to be equivalent to integration by a multilayer network, potentially endowing single neurons with substantial computational power. However, whether neurons can learn to harness dendritic properties to realize this potential is unknown. Here, we develop a learning rule from dendritic cable theory and use it to investigate the processing capacity of a detailed pyramidal neuron model. We show that computations using spatial or temporal features of synaptic input patterns can be learned, and even synergistically combined, to solve a canonical nonlinear feature-binding problem. The voltage dependence of the learning rule drives coactive synapses to engage dendritic nonlinearities, whereas spike-timing dependence shapes the time course of subthreshold potentials. Dendritic input-output relationships can therefore be flexibly tuned through synaptic plasticity, allowing optimal implementation of nonlinear functions by single neurons.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.