Evidence map›Paper›PMID 41896306›Full record

ArticleNature neuroscience2026

The dentate gyrus efficiently converges LEC and MEC inputs into multimodal, highly specific and reliable environmental representations.

Thibault Cholvin, Marlene Bartos

Abstract read
In one paragraph

Article in Nature neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Thibault CholvinInstitute for Physiology I, University of Freiburg, Medical Faculty, Freiburg, Germany. thibault.cholvin@physiologie.uni-freiburg.de.ORCID http://orcid.org/0000-0002-9964-7693
Marlene BartosInstitute for Physiology I, University of Freiburg, Medical Faculty, Freiburg, Germany. marlene.bartos@physiologie.uni-freiburg.de.ORCID http://orcid.org/0000-0001-9741-1946

Funding

Deutsche Forschungsgemeinschaft (German Research Foundation) 537575602Deutsche Forschungsgemeinschaft (German Research Foundation) 561281570EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council) 787450
6 · The paper itself

Abstract

The entorhinal cortex-hippocampal network plays a key role in the processing, storage and retrieval of contextual information. However, how convergent multimodal information provided by the lateral and medial entorhinal cortex (LEC and MEC) is represented by granule cells (GCs), the principal cells of the dentate gyrus (DG), remains poorly understood. In this study, we employed two-photon calcium imaging of LEC and MEC projections in the DG together with GC population activity in mice navigating familiar and novel virtual environments over five consecutive days. We found that LEC inputs primarily convey olfactory information, whereas MEC inputs provide rich context-specific information to the DG. Although environmental representations rapidly emerged in LEC and MEC projections upon exposure to novel environments and remained stable over time, representations in the DG improved gradually and required repeated exposure to stabilize. Our findings suggest that the convergence of rapidly emerging, high-dimensional LEC and MEC inputs into a sparse and slowly evolving GC population code provides an energy-efficient mechanism for generating multimodal, modality-specific and context-specific representations in the DG.

Indexed as

Dentate GyrusEntorhinal CortexEnvironmentAnimalsMaleMiceMice, Inbred C57BLNeural PathwaysNeurons

Identifiers

PMID41896306
PMCPMC13156041

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.