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Linear Summation of Cat Motor Cortex Outputs

2006· article· en· W2162125297 on OpenAlexaff
Christian Éthier, Laurent Brizzi, Warren G. Darling, Charles Capaday

Bibliographic record

VenueJournal of Neuroscience · 2006
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMicrostimulationNeuroscienceForelimbMotor cortexKinematicsStimulationStimulus (psychology)PhysicsPsychology

Abstract

fetched live from OpenAlex

Recruitment of movement-related muscle synergies involves the functional linking of motor cortical points. We asked how the outputs of two simultaneously stimulated motor cortical points would interact. To this end, experiments were done in ketamine-anesthetized cats. When prolonged (e.g., 500 ms) trains of intracortical microstimulation were applied in the primary motor cortex, stimulus currents as low as 10-20 microA evoked coordinated movements of the contralateral forelimb. Paw kinematics in three dimensions and the electromyographic (EMG) activity of eight muscles were simultaneously recorded. We show that the EMG outputs of two cortical points simultaneously stimulated are additive. The movements were represented as displacement vectors pointing from initial to final paw position. The displacement vectors resulting from simultaneous stimulation of two cortical points pointed in nearly the same direction as the algebraic resultant vector. Linear summation of outputs was also found when inhibition at one of the cortical points was reduced by GABAA receptor antagonists. A simple principle emerges from these results. Notwithstanding the underlying complex neuronal circuitry, motor cortex outputs combine nearly linearly in terms of movement direction and muscle activation patterns. Importantly, simultaneous activation does not change the nature of the output at each point. An additional implication is that not all possible movements need be explicitly represented in the motor cortex; a large number of different movements may be synthesized from a smaller repertoire.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.034
GPT teacher head0.266
Teacher spread0.232 · 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 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

Citations73
Published2006
Admission routes1
Has abstractyes

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