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Record W3040408297 · doi:10.1038/s41598-020-67619-w

Developmental onset distinguishes three types of spontaneous recognition memory in mice

2020· article· en· W3040408297 on OpenAlexafffund
Arely Cruz-Sanchez, Shadini Dematagoda, Ridda Ahmed, Sakhithya Mohanathaas, Nicole Odenwald, Maithé Arruda-Carvalho

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRecognition memoryNoveltyCognitionNeuroscienceEpisodic memoryObject (grammar)PsychologyCognitive psychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Spontaneous recognition memory tasks build on an animal's natural preference for novelty to assess the what, where and when components of episodic memory. Their simplicity, ethological relevance and cross-species adaptability make them extremely useful to study the physiology and pathology of memory. Recognition memory deficits are common in rodent models of neurodevelopmental disorders, and yet very little is known about the expression of spontaneous recognition memory in young rodents. This is exacerbated by the paucity of data on the developmental onset of recognition memory in mice, a major animal model of disease. To address this, we characterized the ontogeny of three types of spontaneous recognition memory in mice: object location, novel object recognition and temporal order recognition. We found that object location is the first to emerge, at postnatal day (P)21. This was followed by novel object recognition (24 h delay), at P25. Temporal order recognition was the last to emerge, at P28. Elucidating the developmental expression of recognition memory in mice is critical to improving our understanding of the ontogeny of episodic memory, and establishes a necessary blueprint to apply these tasks to probe cognitive deficits at clinically relevant time points in animal models of developmental disorders.

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)0.002
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 score0.005
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.102
GPT teacher head0.274
Teacher spread0.172 · 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

Citations53
Published2020
Admission routes2
Has abstractyes

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