Evidence map›Paper›PMID 34545249›Full record

ArticleNature neuroscience2021

Mouse visual cortex areas represent perceptual and semantic features of learned visual categories.

Pieter M Goltstein, Sandra Reinert, Tobias Bonhoeffer, Mark Hübener

Abstract read
In one paragraph

Article in Nature neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing 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

28 citing papers in PubMed.

  1. Ecological Visual Processing in the Mouse.Annual review of neuroscience · 2026
    Review
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  3. A collicular-hypothalamic pathway for social visual awareness.bioRxiv : the preprint server for biology · 2026
    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

4 authors.

Pieter M GoltsteinMax Planck Institute of Neurobiology, Martinsried, Germany. goltstein@neuro.mpg.de.ORCID http://orcid.org/0000-0001-8895-0841
Sandra ReinertMax Planck Institute of Neurobiology, Martinsried, Germany.ORCID http://orcid.org/0000-0003-4302-7433
Tobias BonhoefferMax Planck Institute of Neurobiology, Martinsried, Germany.ORCID http://orcid.org/0000-0001-7897-6634
Mark HübenerMax Planck Institute of Neurobiology, Martinsried, Germany. mark@neuro.mpg.de.ORCID http://orcid.org/0000-0001-8367-9132

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Associative memories are stored in distributed networks extending across multiple brain regions. However, it is unclear to what extent sensory cortical areas are part of these networks. Using a paradigm for visual category learning in mice, we investigated whether perceptual and semantic features of learned category associations are already represented at the first stages of visual information processing in the neocortex. Mice learned categorizing visual stimuli, discriminating between categories and generalizing within categories. Inactivation experiments showed that categorization performance was contingent on neuronal activity in the visual cortex. Long-term calcium imaging in nine areas of the visual cortex identified changes in feature tuning and category tuning that occurred during this learning process, most prominently in the postrhinal area (POR). These results provide evidence for the view that associative memories form a brain-wide distributed network, with learning in early stages shaping perceptual representations and supporting semantic content downstream.

Indexed as

AnimalsBrain MappingCalcium SignalingConditioning, OperantDiscrimination, PsychologicalGABA AgonistsGeneralization, PsychologicalLearningMaleMemoryMiceMice, Inbred C57BLMuscimolNeocortexNeuronal PlasticityPhotic StimulationGABA AgonistsMuscimol

Identifiers

PMID34545249
PMCPMC8481127

What OpenQuestion holds

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Registered trials

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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.