Evidence map›Paper›PMID 39568526›Full record

ArticleCognitive neurodynamics2024

Effect of adult hippocampal neurogenesis on pattern separation and its applications.

Zengbin Wang, Kai Yang, Xiaojuan Sun

Abstract read
In one paragraph

Article in Cognitive neurodynamics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Neurobiophilia.Brain sciences · 2026
    Review
  4. Article
  5. 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

3 authors.

Zengbin WangSchool of Science, Beijing University of Posts and Telecommunications, #10 Xitucheng Road, Beijing, 100876 People's Republic of China.
Kai YangSchool of Science, Beijing University of Posts and Telecommunications, #10 Xitucheng Road, Beijing, 100876 People's Republic of China.
Xiaojuan SunSchool of Science, Beijing University of Posts and Telecommunications, #10 Xitucheng Road, Beijing, 100876 People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Adult hippocampal neurogenesis (AHN) is considered essential in memory formation. The dentate gyrus neural network containing newborn dentate gyrus granule cells at the critical period (4-6 weeks) have been widely discussed in neurophysiological and behavioral experiments. However, how newborn dentate gyrus granule cells at this critical period influence pattern separation of dentate gyrus remains unclear. To address this issue, we propose a biologically related dentate gyrus neural network model with AHN. By Leveraging this model, we find pattern separation is enhanced at the medium level of neurogenesis (5% of mature granule cells). This is because the sparse firing of mature granule cells is increased. We can understand this change from the following two aspects. On one hand, newborn granule cells compete with mature granule cells for inputs from the entorhinal cortex, thereby weakening the firing of mature granule cells. On the other hand, newborn granule cells effectively enhance the feedback inhibition level of the network by promoting the firing of interneurons (Mossy cells and Basket cells) and then indirectly regulating the sparse firing of mature granule cells. To verify the validity of the model for pattern separation, we apply the proposed model to a similar concept separation task and reveal that our model outperforms the original model counterparts in this task.

Indexed as

Adult hippocampal neurogenesisDentate gyrus networkPattern separationSimilar concepts separation

Identifiers

PMID39568526
PMCPMC11564429

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.