Evidence map›Paper›PMID 40445322›Full record

ArticleJournal of neural transmission (Vienna, Austria : 1996)2025

Conditional learning increases inhibition of layer 4 excitatory neurons by somatostatin- and parvalbumin-expressing interneurons in the barrel cortex.

Dominik Kanigowski, Joanna Urban-Ciecko

Abstract read
In one paragraph

Article in Journal of neural transmission (Vienna, Austria : 1996), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

2 authors.

Dominik KanigowskiLaboratory of Electrophysiology, Nencki Institute of Experimental Biology PAS, Pasteur Street, Warsaw, 02-093, Poland.ORCID 0000-0001-7565-0508
Joanna Urban-CieckoLaboratory of Electrophysiology, Nencki Institute of Experimental Biology PAS, Pasteur Street, Warsaw, 02-093, Poland. j.ciecko@nencki.edu.pl.ORCID 0000-0003-1676-2336

Funding

National Science Centre, Poland 2015/18/E/NZ4/00721
6 · The paper itself

Abstract

Synaptic strength underlies information processing, learning, and memory storage, yet little is known about how learning impacts synaptic inputs and outputs of specific GABAergic interneurons, particularly in the somatosensory cortex. Using a simple conditional learning model in mice, in which whisker stimulation was paired with a tail shock, we investigated plastic changes in inhibition mediated by somatostatin- (SST-), parvalbumin- (PV-), and vasoactive intestinal polypeptide-expressing interneurons (VIP-INs) in the barrel cortex. In vitro patch-clamp recordings and optogenetics revealed that conditional learning increases inhibition of layer 4 (L4) excitatory neurons by SST- and PV-INs. A small fraction of L4 excitatory neurons in the barrel cortex receives inhibition from local VIP-INs; however, learning does not affect this inhibition. Additionally, learning does not alter excitatory inputs to any of the three interneuron types. These findings suggest that enhanced inhibition by SST-INs and PV-INs may improve information processing and memory coding in L4 of the barrel cortex.

Indexed as

InterneuronsLearningNeural InhibitionParvalbuminsSomatosensory CortexSomatostatinAnimalsExcitatory Postsynaptic PotentialsMaleMiceMice, Inbred C57BLMice, TransgenicOptogeneticsPatch-Clamp TechniquesVasoactive Intestinal PeptideVibrissaeParvalbuminsSomatostatinVasoactive Intestinal PeptideAssociative learningConditioningPseudoconditioningSomatosensory cortexSynaptic plasticityVIP interneurons

Identifiers

PMID40445322
PMCPMC12479588

What Socratic holds

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LicenceCC BY-NC-ND
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 Socratic graph.