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Record W2442940041 · doi:10.1038/srep17697

Self-directed exploration provides a Ncs1-dependent learning bonus

2015· article· en· W2442940041 on OpenAlexafffund
Ho-Suk Mun, Bechara J. Saab, Enoch Ng, Alexander McGirr, Tatiana V. Lipina, Yoichi Gondo, John Georgiou, John Roder

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldNeuroscience
TopicAxon Guidance and Neuronal Signaling
Canadian institutionsUniversity of British ColumbiaLunenfeld-Tanenbaum Research InstituteUniversity of TorontoMount Sinai Hospital
FundersCanadian Institutes of Health ResearchMerz Pharmaceuticals
KeywordsComputer scienceData scienceBioinformaticsBiology

Abstract

fetched live from OpenAlex

Understanding the mechanisms of memory formation is fundamental to establishing optimal educational practices and restoring cognitive function in brain disease. Here, we show for the first time in a non-primate species, that spatial learning receives a special bonus from self-directed exploration. In contrast, when exploration is escape-oriented, or when the full repertoire of exploratory behaviors is reduced, no learning bonus occurs. These findings permitted the first molecular and cellular examinations into the coupling of exploration to learning. We found elevated expression of neuronal calcium sensor 1 (Ncs1) and dopamine type-2 receptors upon self-directed exploration, in concert with increased neuronal activity in the hippocampal dentate gyrus and area CA3, as well as the nucleus accumbens. We probed further into the learning bonus by developing a point mutant mouse (Ncs1(P144S/P144S)) harboring a destabilized NCS-1 protein, and found this line lacked the equivalent self-directed exploration learning bonus. Acute knock-down of Ncs1 in the hippocampus also decoupled exploration from efficient learning. These results are potentially relevant for augmenting learning and memory in health and disease, and provide the basis for further molecular and circuit analyses in this direction.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.115
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.075
GPT teacher head0.283
Teacher spread0.208 · 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

Citations34
Published2015
Admission routes2
Has abstractyes

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