Evidence map›Paper›PMID 38509348›Full record

ArticleNature neuroscience2024

Co-dependent excitatory and inhibitory plasticity accounts for quick, stable and long-lasting memories in biological networks.

Everton J Agnes, Tim P Vogels

Abstract read
In one paragraph

Article in Nature neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

  1. Homeostatic Plasticity Enables Stable yet Tunable Neuronal Assemblies.bioRxiv : the preprint server for biology · 2026
    Article
  2. An integratediScience · 2026
    Article
  3. Neuroligin-2-Dependent Adhesion Defines a Molecular Checkpoint for Inhibitory Synaptic Plasticity.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026
    Article
  4. Article
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  7. Sharpened Visual Memory Representations Are Reflected in Inferotemporal Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026
    Article
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  16. Synapse-type-specific competitive Hebbian learning forms functional recurrent networks.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
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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

2 authors.

Everton J AgnesCentre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK. everton.agnes@gmail.com.ORCID http://orcid.org/0000-0001-7184-7311
Tim P VogelsCentre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0003-3295-6181

Funding

EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) SYNAPSEEKLeverhulme Trust RPG-2016-446Wellcome TrustWellcome Trust (Wellcome) 214316/Z/18/ZWellcome Trust (Wellcome) WT100000
6 · The paper itself

Abstract

The brain's functionality is developed and maintained through synaptic plasticity. As synapses undergo plasticity, they also affect each other. The nature of such 'co-dependency' is difficult to disentangle experimentally, because multiple synapses must be monitored simultaneously. To help understand the experimentally observed phenomena, we introduce a framework that formalizes synaptic co-dependency between different connection types. The resulting model explains how inhibition can gate excitatory plasticity while neighboring excitatory-excitatory interactions determine the strength of long-term potentiation. Furthermore, we show how the interplay between excitatory and inhibitory synapses can account for the quick rise and long-term stability of a variety of synaptic weight profiles, such as orientation tuning and dendritic clustering of co-active synapses. In recurrent neuronal networks, co-dependent plasticity produces rich and stable motor cortex-like dynamics with high input sensitivity. Our results suggest an essential role for the neighborly synaptic interaction during learning, connecting micro-level physiology with network-wide phenomena.

Indexed as

Models, NeurologicalNerve NetNeuronal PlasticitySynapsesAnimalsHumansLong-Term PotentiationMemoryNeural InhibitionNeurons

Identifiers

PMID38509348
PMCPMC11089004

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.