Evidence map›Paper›PMID 26424883›Full record

ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2015

Fast Learning with Weak Synaptic Plasticity.

Pierre Yger, Marcel Stimberg, Romain Brette

Abstract read
In one paragraph

Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

  1. Article
  2. Homeostatic Plasticity Enables Stable yet Tunable Neuronal Assemblies.bioRxiv : the preprint server for biology · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. Estimated gray matter volume rapidly changes after a short motor task.Cerebral cortex (New York, N.Y. : 1991) · 2022
    Article
  7. Review
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  16. Article
  17. Natural Firing Patterns Imply Low Sensitivity of Synaptic Plasticity to Spike Timing Compared with Firing Rate.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2016
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Pierre YgerInstitut d'Etudes de la Cognition, Ecole Normale Supérieure, 75005 Paris, France, Sorbonne Université, UPMC Université Paris 06 UMRS968, 75006 Paris, France, and Institut de la Vision, INSERM U968, CNRS UMR7210, 75012 Paris, France pierre.yger@inserm.fr.
Marcel StimbergSorbonne Université, UPMC Université Paris 06 UMRS968, 75006 Paris, France, and Institut de la Vision, INSERM U968, CNRS UMR7210, 75012 Paris, France.ORCID http://orcid.org/0000-0002-2648-4790
Romain BretteSorbonne Université, UPMC Université Paris 06 UMRS968, 75006 Paris, France, and Institut de la Vision, INSERM U968, CNRS UMR7210, 75012 Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

New sensory stimuli can be learned with a single or a few presentations. Similarly, the responses of cortical neurons to a stimulus have been shown to increase reliably after just a few repetitions. Long-term memory is thought to be mediated by synaptic plasticity, but in vitro experiments in cortical cells typically show very small changes in synaptic strength after a pair of presynaptic and postsynaptic spikes. Thus, it is traditionally thought that fast learning requires stronger synaptic changes, possibly because of neuromodulation. Here we show theoretically that weak synaptic plasticity can, in fact, support fast learning, because of the large number of synapses N onto a cortical neuron. In the fluctuation-driven regime characteristic of cortical neurons in vivo, the size of membrane potential fluctuations grows only as √N, whereas a single output spike leads to potentiation of a number of synapses proportional to N. Therefore, the relative effect of a single spike on synaptic potentiation grows as √N. This leverage effect requires precise spike timing. Thus, the large number of synapses onto cortical neurons allows fast learning with very small synaptic changes. Significance statement: Long-term memory is thought to rely on the strengthening of coactive synapses. This physiological mechanism is generally considered to be very gradual, and yet new sensory stimuli can be learned with just a few presentations. Here we show theoretically that this apparent paradox can be solved when there is a tight balance between excitatory and inhibitory input. In this case, small synaptic modifications applied to the many synapses onto a given neuron disrupt that balance and produce a large effect even for modifications induced by a single stimulus. This effect makes fast learning possible with small synaptic changes and reconciles physiological and behavioral observations.

Indexed as

AlgorithmsCerebral CortexComputer SimulationElectrophysiological PhenomenaExcitatory Postsynaptic PotentialsFeedback, SensoryHumansLearningMemoryModels, NeurologicalNeural PathwaysNeuronal PlasticityNeuronsSynapseslearningnetworkperceptual learningspike-timing-dependent plasticity

Identifiers

PMID26424883
PMCPMC6605471

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.