Evidence map›Paper›PMID 35362411›Full record

ArticleeLife2022

A dynamic clamp protocol to artificially modify cell capacitance.

Paul Pfeiffer, Federico José Barreda Tomás, Jiameng Wu, Jan-Hendrik Schleimer, Imre Vida, Susanne Schreiber

Abstract read
In one paragraph

Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Observing hidden neuronal states in experiments.PLoS computational biology · 2025
    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

6 authors.

Paul PfeifferInstitute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany.ORCID 0000-0001-5324-5886
Federico José Barreda TomásBernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin, Berlin, Germany.
Jiameng WuBernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin, Berlin, Germany.ORCID 0000-0002-6266-7666
Jan-Hendrik SchleimerInstitute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany.ORCID 0000-0002-2156-330X
Imre VidaBernstein Center for Computational Neuroscience, Humboldt-Universität zu Berlin, Berlin, Germany.ORCID 0000-0003-3214-2233
Susanne SchreiberInstitute for Theoretical Biology, Humboldt-Universität zu Berlin, Berlin, Germany.ORCID 0000-0003-3913-5650

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dynamics of excitable cells and networks depend on the membrane time constant, set by membrane resistance and capacitance. Whereas pharmacological and genetic manipulations of ionic conductances of excitable membranes are routine in electrophysiology, experimental control over capacitance remains a challenge. Here, we present capacitance clamp, an approach that allows electrophysiologists to mimic a modified capacitance in biological neurons via an unconventional application of the dynamic clamp technique. We first demonstrate the feasibility to quantitatively modulate capacitance in a mathematical neuron model and then confirm the functionality of capacitance clamp in in vitro experiments in granule cells of rodent dentate gyrus with up to threefold virtual capacitance changes. Clamping of capacitance thus constitutes a novel technique to probe and decipher mechanisms of neuronal signaling in ways that were so far inaccessible to experimental electrophysiology.

Indexed as

BrainNeuronsMembrane PotentialsPatch-Clamp Techniquesclosed-loop feedbackdynamic clampmembrane capacitanceneuronal morphologyneuroscienceratsingle neuron dynamics

Identifiers

PMID35362411
PMCPMC9135398

What OpenQuestion holds

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LicenceCC BY
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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.