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Record W7107964869 · doi:10.5061/dryad.2rbnzs81t

Visual objects refine head direction coding

2025· dataset· en· W7107964869 on OpenAlexafffund

Bibliographic record

VenueDRYAD · 2025
Typedataset
Languageen
Field
Topic
Canadian institutionsMila - Quebec Artificial Intelligence InstituteMcGill University
FundersCanadian Institutes of Health ResearchSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungDeutsche Forschungsgemeinschaft
KeywordsCoding (social sciences)Neural codingVisual cortexVisual processingBrain activity and meditationVisual perceptionVisualizationObject (grammar)ElectrophysiologySaliency map

Abstract

fetched live from OpenAlex

Animals use visual objects to guide navigation-related behaviors, from hunting prey to escaping predators and exploring the world. However, little is known about where visual objects are encoded in the mouse brain or how they impact processing in the spatial navigation system. Using functional ultrasound imaging, we conducted a brain-wide screen and identified areas preferentially activated by images of objects compared to scrambled versions. While visual cortical areas did not show a significant preference, brain areas associated with spatial navigation were preferentially activated by visual objects. Electrophysiological recordings in the postsubiculum, the primary cortical area of the head direction (HD) system, confirmed a preference for visual objects in both HD cells and fast-spiking interneurons. Finally, we found that visual objects dynamically modulated HD cells in freely moving animals, selectively increasing firing rates of HD cells aligned with a visual object while decreasing activity in HD cells coding for other directions.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.005

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 head0.019
GPT teacher head0.346
Teacher spread0.327 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2025
Admission routes2
Has abstractyes

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