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Unconventional NMDA Receptor Signaling

2017· review· en· W2777541397 on OpenAlexafffund
Kim Doré, Ivar S. Stein, Jennifer A Brock, Pablo E. Castillo, Karen Zito, P. Jesper Sjöström

Bibliographic record

VenueJournal of Neuroscience · 2017
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsMcGill UniversityMcGill University Health CentreMontreal General Hospital
FundersNational Institute on Drug AbuseNational Institute of Neurological Disorders and StrokeNational Institute of Mental HealthBiotechnology and Biological Sciences Research CouncilCanadian Institutes of Health ResearchNational Institute on AgingNational Institutes of Health
KeywordsCoincidence detection in neurobiologyNMDA receptorNeuroscienceHebbian theoryPostsynaptic potentialSIGNAL (programming language)IfenprodilNeurotransmissionReceptorChemistryBiologyComputer scienceCoincidenceArtificial neural networkArtificial intelligenceMedicineBiochemistry

Abstract

fetched live from OpenAlex

In the classical view, NMDA receptors (NMDARs) are stably expressed at the postsynaptic membrane, where they act via Ca 2+ to signal coincidence detection in Hebbian plasticity. More recently, it has been established that NMDAR-mediated transmission can be dynamically regulated by neural activity. In addition, NMDARs have been found presynaptically, where they cannot act as conventional coincidence detectors. Unexpectedly, NMDARs have also been shown to signal metabotropically, without the need for Ca 2+ . This review highlights novel findings concerning these unconventional modes of NMDAR action.

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)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0050.001
Research integrity0.0000.002
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 head0.411
GPT teacher head0.500
Teacher spread0.089 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations142
Published2017
Admission routes2
Has abstractyes

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