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Record W2273912701 · doi:10.1038/ncomms10838

Neurogenesis-mediated forgetting minimizes proactive interference

2016· article· en· W2273912701 on OpenAlexafffund
Jonathan R. Epp, Rudy Silva Mera, Stefan Köhler, Sheena A. Josselyn, Paul W. Frankland

Bibliographic record

VenueNature Communications · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeurogenesis and neuroplasticity mechanisms
Canadian institutionsUniversity of TorontoBaycrest HospitalWestern UniversityHospital for Sick Children
FundersCanadian Institutes of Health ResearchHospital for Sick Children
KeywordsNeurogenesisForgettingHippocampal formationEncoding (memory)Interference theoryComputer scienceNeuroscienceInterference (communication)PsychologyCognitionCognitive psychologyComputer networkWorking memory

Abstract

fetched live from OpenAlex

Established memories may interfere with the encoding of new memories, particularly when existing and new memories overlap in content. By manipulating levels of hippocampal neurogenesis, here we show that neurogenesis regulates this form of proactive interference. Increasing hippocampal neurogenesis weakens existing memories and, in doing so, facilitates the encoding of new, conflicting (but not non-conflicting) information in mice. Conversely, decreasing neurogenesis stabilizes existing memories, and impedes the encoding of new, conflicting information. These results suggest that reduced proactive interference is an adaptive benefit of neurogenesis-induced forgetting.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.056
GPT teacher head0.301
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations231
Published2016
Admission routes2
Has abstractyes

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