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Record W2412643582 · doi:10.1016/j.celrep.2016.05.029

A Specific Population of Reticulospinal Neurons Controls the Termination of Locomotion

2016· article· en· W2412643582 on OpenAlexafffund
Laurent Juvin, Swantje Grätsch, Émilie Trillaud-Doppia, Jean‐François Gariépy, Ansgar Büschges, Réjean Dubuc

Bibliographic record

VenueCell Reports · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsUniversité du Québec à MontréalUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsNeuroscienceBrainstemBiologyPopulationLampreyPremovement neuronal activityMedicine

Abstract

fetched live from OpenAlex

Locomotion requires the proper sequencing of neural activity to start, maintain, and stop it. Recently, brainstem neurons were shown to specifically stop locomotion in mammals. However, the cellular properties of these neurons and their activity during locomotion are still unknown. Here, we took advantage of the lamprey model to characterize the activity of a cell population that we now show to be involved in stopping locomotion. We find that these neurons display a burst of spikes that coincides with the end of swimming activity. Their pharmacological activation ends ongoing swimming, whereas the inactivation of these neurons dramatically impairs the rapid termination of swimming. These neurons are henceforth referred to as stop cells, because they play a crucial role in the termination of locomotion. Our findings contribute to the fundamental understanding of motor control and provide important details about the cellular mechanisms involved in locomotor termination.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.264
Teacher spread0.254 · 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 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

Citations75
Published2016
Admission routes2
Has abstractyes

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