Evidence map›Paper›PMID 32453795›Full record

ArticlePLoS computational biology2020

Chloride dynamics alter the input-output properties of neurons.

Christopher B Currin, Andrew J Trevelyan, Colin J Akerman, Joseph V Raimondo

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
3.0field-weighted citation impact, top 8% of its field
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

25 citing papers in PubMed, 40 citations in OpenAlex.

  1. Article
  2. Review
  3. Direct energy consumption by inhibitory GABAFrontiers in neuroscience · 2026
    Article
  4. Article
  5. Review
  6. Article
  7. Optogenetic Determination of Dynamic and Cell-Type-Specific Inhibitory Reversal Potentials.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024
    Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Dynamics of ramping bursts in a respiratory neuron model.Journal of computational neuroscience · 2022
    Article
  16. Article
  17. NKCC1 and KCC2: Structural insights into phospho-regulation.Frontiers in molecular neuroscience · 2022
    Review
  18. Article
  19. Intricacies of GABAInternational journal of molecular sciences · 2021
    Review
  20. When Are Depolarizing GABAergic Responses Excitatory?Frontiers in molecular neuroscience · 2021
    Review
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

4 authors at 3 institutions in 2 countries.

Christopher B CurrinDivision of Cell Biology, Department of Human Biology, Neuroscience Institute and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.ORCID 0000-0002-4809-5059
Andrew J TrevelyanInstitute of Neuroscience, Newcastle University, Newcastle upon Tyne, United Kingdom.ORCID 0000-0001-9307-4241
Colin J AkermanDepartment of Pharmacology, University of Oxford, Oxford, United Kingdom.
Joseph V RaimondoDivision of Cell Biology, Department of Human Biology, Neuroscience Institute and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.
University of Cape Town · ZANewcastle University · GBUniversity of Oxford · GB

Funding

Biotechnology and Biological Sciences Research Council BB/P019854/1European Research Council 617670Medical Research Council MR/J013250/1Medical Research Council MR/R005427/1Wellcome Trust
6 · The paper itself

Abstract

Fast synaptic inhibition is a critical determinant of neuronal output, with subcellular targeting of synaptic inhibition able to exert different transformations of the neuronal input-output function. At the receptor level, synaptic inhibition is primarily mediated by chloride-permeable Type A GABA receptors. Consequently, dynamics in the neuronal chloride concentration can alter the functional properties of inhibitory synapses. How differences in the spatial targeting of inhibitory synapses interact with intracellular chloride dynamics to modulate the input-output function of neurons is not well understood. To address this, we developed computational models of multi-compartment neurons that incorporate experimentally parametrised mechanisms to account for neuronal chloride influx, diffusion, and extrusion. We found that synaptic input (either excitatory, inhibitory, or both) can lead to subcellular variations in chloride concentration, despite a uniform distribution of chloride extrusion mechanisms. Accounting for chloride changes resulted in substantial alterations in the neuronal input-output function. This was particularly the case for peripherally targeted dendritic inhibition where dynamic chloride compromised the ability of inhibition to offset neuronal input-output curves. Our simulations revealed that progressive changes in chloride concentration mean that the neuronal input-output function is not static but varies significantly as a function of the duration of synaptic drive. Finally, we found that the observed effects of dynamic chloride on neuronal output were mediated by changes in the dendritic reversal potential for GABA. Our findings provide a framework for understanding the computational effects of chloride dynamics on dendritically targeted synaptic inhibition.

Indexed as

Action PotentialsAnimalsBrainChloridesComputer SimulationDendritesHippocampusHumansKineticsMaleModels, NeurologicalNeuronsOrgan Culture TechniquesProtein BindingPyramidal CellsRatsChloridesReceptors, GABAReceptors, GABA-A

Identifiers

PMID32453795
PMCPMC7307785
OpenAlexW3030060774

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.