MétaCan
Menu
Back to cohort
Record W4211105450 · doi:10.1126/sciadv.abi9071

Forgetting generates a novel state that is reactivatable

2022· article· en· W4211105450 on OpenAlexaff
He Liu, Taihong Wu, Xicotencatl Gracida Canales, Min Wu, Myung-Kyu Choi, Fengyun Duan, John A. Calarco, Yun Zhang

Bibliographic record

VenueScience Advances · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsUniversity of Toronto
FundersNational Institute on Deafness and Other Communication DisordersNational Institute of Neurological Disorders and StrokeNational Institute of Mental HealthNational Institutes of Health
KeywordsForgettingNeuroscienceAMPA receptorState (computer science)Computer scienceNeuronPsychologyBiologyCognitive psychologyReceptorNMDA receptorGenetics

Abstract

fetched live from OpenAlex

Forgetting is defined as a time-dependent decline of a memory. However, it is not clear whether forgetting reverses the learning process to return the brain to the naive state. Here, using the aversive olfactory learning of pathogenic bacteria in C. elegans , we show that forgetting generates a novel state of the nervous system that is distinct from the naive state or the learned state. A transient exposure to the training condition or training odorants reactivates this novel state to elicit the previously learned behavior. An AMPA receptor and a type II serotonin receptor act in the central neuron of the learning circuit to decrease and increase the speed to reach this novel state, respectively. Together, our study systematically characterizes forgetting and uncovers conserved mechanisms underlying the rate of 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)0.000
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 score0.094
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.260
Teacher spread0.244 · 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

Citations40
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueScience AdvancesSame topicGenetics, Aging, and Longevity in Model OrganismsFrench-language works237,207