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Record W2259538044 · doi:10.1152/jn.2000.83.1.630

Comparing Extraocular Motoneuron Discharges During Head-Restrained Saccades and Head-Unrestrained Gaze Shifts

2000· article· en· W2259538044 on OpenAlexaff
Kathleen E. Cullen, Henrietta L. Galiana, Pierre A. Sylvestre

Bibliographic record

VenueJournal of Neurophysiology · 2000
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill University
Fundersnot available
KeywordsGazeEye movementReticular formationExtraocular musclesNeurosciencePsychologyAbducens nucleusHead (geology)Oculomotor nucleusParamedian pontine reticular formationCommunicationAnatomyNucleusMidbrainCentral nervous systemBiology

Abstract

fetched live from OpenAlex

Burst neurons (BNs) in the paramedian pontine reticular formation provide the primary input to the extraocular motoneurons (MNs) during head-restrained saccades and combined eye-head gaze shifts. Prior studies have shown that BNs carry eye movement-related signals during saccades and carry head as well as eye movement-related signals during gaze shifts. Therefore MNs receive signals related to head motion during gaze shifts, yet they solely drive eye motion. Here we addressed whether the relationship between MN firing rates and eye movements is influenced by the additional premotor signals present during gaze shifts. Neurons in the abducens nucleus of monkeys were first studied during saccades made with the head stationary. We then recorded from the same neurons during voluntary combined eye-head gaze shifts. We conclude that the activity of MNs, in contrast to that of BNs, is related to eye motion by the same dynamic relationship during head-restrained saccades and head-unrestrained gaze shifts. In addition, we show that a standard metric-based analysis [i.e., counting the number of spikes (NOS) in a burst] yields misleading results when applied to the same data set. We argue that this latter approach fails because it does not properly consider the system's dynamics or the strong interactions between eye and head motion.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.068
GPT teacher head0.327
Teacher spread0.259 · 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

Citations22
Published2000
Admission routes1
Has abstractyes

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