Evidence map›Paper›PMID 32434549›Full record

ReviewMolecular brain2020

A two-compartment model of synaptic computation and plasticity.

Rudi Tong, Nigel J Emptage, Zahid Padamsey

Abstract readReview
In one paragraph

Review in Molecular brain, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Review
  2. Article
  3. VIP-to-SST Cell Circuit Motif Shows Differential Short-Term Plasticity across Sensory Areas of Mouse Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Article
  4. GluN2A- and GluN2B-containing pre-synapticPhilosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024
    Article
  5. Article
  6. Review
  7. Article
  8. Review
  9. Article
  10. 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.

Rudi TongDepartment of Pharmacology, University of Oxford, Mansfield Road, Oxford, OX1 3QT, UK. rudi.tong@mcgill.ca.ORCID 0000-0002-2640-2898
Nigel J EmptageDepartment of Pharmacology, University of Oxford, Mansfield Road, Oxford, OX1 3QT, UK. nigel.emptage@pharm.ox.ac.uk.
Zahid PadamseyCentre of Discovery Brain Sciences, University of Edinburgh, 9 George Square, Edinburgh, EH8 9XD, UK. zahid.padamsey@ed.ac.uk.

Funding

Medical Research Council
6 · The paper itself

Abstract

The synapse is typically viewed as a single compartment, which acts as a linear gain controller on incoming input. Traditional plasticity rules enable this gain control to be dynamically optimized by Hebbian activity. Whilst this view nicely captures postsynaptic function, it neglects the non-linear dynamics of presynaptic function. Here we present a two-compartment model of the synapse in which the presynaptic terminal first acts to filter presynaptic input before the postsynaptic terminal, acting as a gain controller, amplifies or depresses transmission. We argue that both compartments are equipped with distinct plasticity rules to enable them to optimally adapt synaptic transmission to the statistics of pre- and postsynaptic activity. Specifically, we focus on how presynaptic plasticity enables presynaptic filtering to be optimally tuned to only transmit information relevant for postsynaptic firing. We end by discussing the advantages of having a presynaptic filter and propose future work to explore presynaptic function and plasticity in vivo.

Indexed as

AnimalsHumansModels, NeurologicalNerve NetNeuronal PlasticityNeuronsSynapsesSynaptic Transmission

Identifiers

PMID32434549
PMCPMC7238589

What OpenQuestion holds

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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.