Visual objects refine head direction coding
Bibliographic record
Abstract
Abstract Animals use visual objects to guide navigation-related behaviors, from hunting prey, to escaping predators, to 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 in mice, we conducted a brain-wide screen and identified brain areas that were preferentially activated by images of objects compared to scrambled versions of the same images. While visual cortical areas did not show a significant preference, regions associated with spatial navigation were preferentially activated by visual objects. Electrophysiological recordings in postsubiculum, the primary cortical area of the head direction (HD) system, further confirmed a preference for visual objects, which was present in both HD cells and fast-spiking interneurons. Finally, we found that visual objects dynamically modulated HD cells, selectively increasing firing rates of HD cells aligned with a visual landmark’s direction, while decreasing activity in HD cells coding for other directions. These results reveal that visual objects refine population-level coding of head direction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".