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Record W2992576483 · doi:10.1523/eneuro.0359-18.2019

Timing Determines Tuning: A Rapid Spatial Transformation in Superior Colliculus Neurons during Reactive Gaze Shifts

2019· article· en· W2992576483 on OpenAlexaff
Morteza Sadeh, Amirsaman Sajad, Hongying Wang, Xiaogang Yan, J. Douglas Crawford

Bibliographic record

VenueeNeuro · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsYork University
Fundersnot available
KeywordsGazeSuperior colliculusStimulus (psychology)NeuroscienceComputer scienceTransformation (genetics)PrimateEye movementPsychologyCommunicationArtificial intelligenceCognitive psychologyBiology

Abstract

fetched live from OpenAlex

Abstract Gaze saccades, rapid shifts of the eyes and head toward a goal, have provided fundamental insights into the neural control of movement. For example, it has been shown that the superior colliculus (SC) transforms a visual target (T) code to future gaze (G) location commands after a memory delay. However, this transformation has not been observed in “reactive” saccades made directly to a stimulus, so its contribution to normal gaze behavior is unclear. Here, we tested this using a quantitative measure of the intermediate codes between T and G, based on variable errors in gaze endpoints. We demonstrate that a rapid spatial transformation occurs within the primate’s SC ( Macaca mulatta ) during reactive saccades, involving a shift in coding from T, through intermediate codes, to G. This spatial shift progressed continuously both across and within cell populations [visual, visuomotor (VM), motor], rather than relaying discretely between populations with fixed spatial codes. These results suggest that the SC produces a rapid, noisy, and distributed transformation that contributes to variable errors in reactive gaze shifts.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.650

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 integrity0.0000.000
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 head0.021
GPT teacher head0.235
Teacher spread0.214 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

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