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Record W2614253577 · doi:10.1101/lm.044750.116

Medial prefrontal cortex and dorsomedial striatum are necessary for the trial-unique, delayed nonmatching-to-location (TUNL) task in rats: role of NMDA receptors

2017· article· en· W2614253577 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueLearning & Memory · 2017
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNMDA receptorNeurosciencePsychologyPrefrontal cortexAntagonistStriatumReceptorChemistryCognitionDopamine

Abstract

fetched live from OpenAlex

The trial-unique, delayed nonmatching-to-location (TUNL) task is a recently developed behavioral task that measures spatial working memory and a form of pattern separation in touchscreen-equipped operant conditioning chambers. Limited information exists regarding the neurotransmitters and neural substrates involved in the task. The present experiments tested the effects of systemic and intracranial injections of NMDA receptor antagonists on the TUNL task. After training, male Long Evans rats systemically injected with the competitive NMDA receptor antagonist CPP (10 mg/kg) had impaired accuracy regardless of the degree of stimuli separation or length of delay between the sample and test phases. Injections of Ro 25-6981 (6 or 10 mg/kg), an antagonist selective for GluN2B subunit-containing NMDA receptors, did not affect accuracy on the task. Direct infusion of the competitive NMDA receptor antagonist AP5 into mPFC or dmSTR reduced overall accuracy on the TUNL task. These results demonstrate that TUNL task performance depends on NMDA receptors within the mPFC and dmSTR.

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.

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)0.001
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 score0.011
Threshold uncertainty score0.689

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
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 integrity0.0000.001
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 head0.037
GPT teacher head0.304
Teacher spread0.267 · 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