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Record W3165921463 · doi:10.3389/fncir.2021.638007

Locus Coeruleus Norepinephrine in Learned Behavior: Anatomical Modularity and Spatiotemporal Integration in Targets

2021· review· en· W3165921463 on OpenAlexfundno aff
Vincent Breton‐Provencher, Gabrielle T Drummond, Mriganka Sur

Bibliographic record

VenueFrontiers in Neural Circuits · 2021
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsnot available
FundersNational Eye InstituteNational Institute of Mental HealthNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsLocus coeruleusNeuroscienceBrainstemForebrainArousalNorepinephrinePsychologyContext (archaeology)Sensory systemBiologyCentral nervous systemDopamine

Abstract

fetched live from OpenAlex

The locus coeruleus (LC), a small brainstem nucleus, is the primary source of the neuromodulator norepinephrine (NE) in the brain. The LC receives input from widespread brain regions, and projects throughout the forebrain, brainstem, cerebellum, and spinal cord. LC neurons release NE to control arousal, but also in the context of a variety of sensory-motor and behavioral functions. Despite its brain-wide effects, much about the role of LC-NE in behavior and the circuits controlling LC activity is unknown. New evidence suggests that the modular input-output organization of the LC could enable transient, task-specific modulation of distinct brain regions. Future work must further assess whether this spatial modularity coincides with functional differences in LC-NE subpopulations acting at specific times, and how such spatiotemporal specificity might influence learned behaviors. Here, we summarize the state of the field and present new ideas on the role of LC-NE in learned behaviors.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.110
GPT teacher head0.388
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations169
Published2021
Admission routes1
Has abstractyes

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