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Record W4214806936 · doi:10.1093/cercor/bhac068

A secondary motor area contributing to interlimb coordination during visually guided locomotion in the cat

2022· article· en· W4214806936 on OpenAlexafffund
Toshi Nakajima, Nicolas Fortier-Lebel, Trevor Drew

Bibliographic record

VenueCerebral Cortex · 2022
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversité de Montréal
FundersJapan Society for the Promotion of ScienceCanadian Institutes of Health Research
KeywordsForelimbHindlimbNeuroscienceAnatomyBiologyTreadmillPsychology

Abstract

fetched live from OpenAlex

We investigated the contribution of cytoarchitectonic cortical area 4δc, in the caudal bank of the cruciate sulcus of the cat, to the control of visually guided locomotion. To do so, we recorded the activity of 114 neurons in 4δc while cats walked on a treadmill and stepped over an obstacle that advanced toward them. A total of 84/114 (74%) cells were task-related and 68/84 (81%) of these cells showed significant modulation of their discharge frequency when the contralateral limbs were the first to step over the obstacle. These latter cells included a substantial proportion (27/68 40%) that discharged between the passage of the contralateral forelimb and the contralateral hindlimb over the obstacle, suggesting a contribution of this area to interlimb coordination. We further compared the discharge in area 4δc with the activity patterns of cells in the rostral division of the same cytoarchitectonic area (4δr), which has been suggested to be a separate functional region. Despite some differences in the patterns of activity in the 2 subdivisions, we suggest that activity in each is compatible with a contribution to interlimb coordination and that they should be considered as a single functional area that contributes to both forelimb-forelimb and forelimb-hindlimb coordination.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.875
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.011
GPT teacher head0.226
Teacher spread0.215 · 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 designObservational
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

Citations10
Published2022
Admission routes2
Has abstractyes

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