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Record W2092797243 · doi:10.1017/s0952523807070113

Neuronal response to texture- and contrast-defined boundaries in early visual cortex

2007· article· en· W2092797243 on OpenAlexafffund
Yuning Song, Curtis L. Baker

Bibliographic record

VenueVisual Neuroscience · 2007
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsContrast (vision)Visual cortexTexture (cosmology)NeuroscienceSpatial frequencyLuminanceSurround suppressionVisual perceptionReceptive fieldPerceptionNeuronVisual systemPsychologyCommunicationOrientation (vector space)Artificial intelligenceOpticsComputer sciencePhysicsMathematicsGeometry

Abstract

fetched live from OpenAlex

Natural scenes contain a variety of visual cues that facilitate boundary perception (e.g., luminance, contrast, and texture). Here we explore whether single neurons in early visual cortex can process both contrast and texture cues. We recorded neural responses in cat A18 to both illusory contours formed by abutting gratings (ICs, texture-defined) and contrast-modulated gratings (CMs, contrast-defined). We found that if a neuron responded to one of the two stimuli, it also responded to the other. These neurons signaled similar contour orientation, spatial frequency, and movement direction of the two stimuli. A given neuron also exhibited similar selectivity for spatial frequency of the fine, stationary grating components (carriers) of the stimuli. These results suggest that the cue-invariance of early cortical neurons extends to different kinds of texture or contrast cues, and might arise from a common nonlinear mechanism.

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)0.000
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.342
Teacher spread0.308 · 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
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

Citations31
Published2007
Admission routes2
Has abstractyes

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