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Record W2088945375 · doi:10.1167/8.6.806

A new method for determining neuron receptive field reference-frames

2010· article· en· W2088945375 on OpenAlexaffabout
G. P. Keith, Joseph F. X. DeSouza, Xiang Yan, H. Wang, J. Douglas Crawford

Bibliographic record

VenueJournal of Vision · 2010
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsYork University
Fundersnot available
KeywordsReference frameSaccadeComputer scienceGazeReceptive fieldFrame of referenceComputer visionArtificial intelligenceFixation (population genetics)Head (geology)Superior colliculusEye movementFrame (networking)NeurosciencePhysicsPsychologyGeology

Abstract

fetched live from OpenAlex

Mapping neuron receptive fields (RFs) only leads to an understanding of the role these neurons play in sensorimotor behavior if the proper reference-frame of each RF is determined as well. Our goal was to develop a method by which this reference-frame could be determined from the RF mapping itself. We developed this method using a data set that included single-unit recordings from 80 neurons in the intermediate superior colliculus of two head-unrestrained monkeys during performance of a visual saccade task. Using head-unrestrained animals represents an improvement over head-restrained animals in that head movements allow a more natural behavior and open up an additional reference frame in which neuron RFs might be represented. Because the torsional component of eye and head are significant in this condition, we recorded both positions in 3-D using dual search coils. We plotted RFs using both the final gaze and visual target directions relative to initial gaze fixation, in eye, head and space reference-frames. While the requirements of target fixation constrained both initial and final gaze directions, freedom of movement of both eye and head produced a natural variation in these orientations across successive trials. This meant that the RFs plotted in different reference frames would themselves be different. We modeled this using hypothetical RFs, and found that these variations produced a ‘smearing’ of the RF in all reference frames other than the proper reference-frame associated with the neuron's activity. A non-parametric fitting of each RF in the different reference-frames, and a Levine test comparing the variances of the residuals of these fits, produced a significant difference when eye and head orientation variability were of a size similar to that shown by monkeys performing saccade tasks, so that the proper reference-frame could be distinguished. We confirmed this using our actual neurophysiological data. Supported by: CIHR (Canada). JDC holds a Canada Research Chair.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.091
GPT teacher head0.438
Teacher spread0.347 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations0
Published2010
Admission routes2
Has abstractyes

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