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Record W4304780429 · doi:10.1016/j.isci.2022.105335

Optokinetic set-point adaptation functions as an internal dynamic calibration mechanism for oculomotor disequilibrium

2022· article· en· W4304780429 on OpenAlexafffund
Ting‐Feng Lin, Mohammad Hossain Mohammadi, Kathleen E. Cullen, Maurice J. Chacron, Yingyu Huang

Bibliographic record

VenueiScience · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsMcGill University
FundersNational Institutes of HealthUniversität ZürichInselspital, Universitätsspital BernCanadian Institutes of Health ResearchEMDO StiftungMcGill University
KeywordsOptokinetic reflexAdaptation (eye)Sensory AdaptationNeuroscienceSet (abstract data type)Mechanism (biology)PsychologyEye movementSensory systemComputer sciencePhysics

Abstract

fetched live from OpenAlex

Experience-dependent brain circuit plasticity underlies various sensorimotor learning and memory processes. Recently, a novel set-point adaptation mechanism was identified that accounts for the pronounced negative optokinetic afternystagmus (OKAN) following a sustained period of unidirectional optokinetic nystagmus (OKN) in larval zebrafish. To investigate the physiological significance of optokinetic set-point adaptation, animals in the current study were exposed to a direction-alternating optokinetic stimulation paradigm that better resembles their visual experience in nature. Our results reveal that not only was asymmetric alternating stimulation sufficient to induce the set-point adaptation and the resulting negative OKAN, but most strikingly, under symmetric alternating stimulation some animals displayed an inherent bias of the OKN gain in one direction, and that was compensated by the similar set-point adaptation. This finding, supported by mathematical modeling, suggests that set-point adaptation allows animals to cope with asymmetric optokinetic behaviors evoked by either external stimuli or innate oculomotor biases.

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 score0.949
Threshold uncertainty score0.639

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 studies0.0010.000
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 head0.029
GPT teacher head0.275
Teacher spread0.246 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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