Cortical connectivity maps reveal anatomically distinct areas in the parietal cortex of the rat
Why this work is in the frame
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Bibliographic record
Abstract
A central feature of theories of spatial navigation involves the representation of spatial relationships between objects in complex environments. The parietal cortex has long been linked to the processing of spatial visual information and recent evidence from single unit recording in rodents suggests a role for this region in encoding egocentric and world-centered frames. The rat parietal cortex can be subdivided into four distinct rostral-caudal and medial-lateral regions, which includes a zone previously characterized as secondary visual cortex. At present, very little is known regarding the relative connectivity of these parietal subdivisions. Thus, we set out to map the connectivity of the entire anterior-posterior and medial-lateral span of this region. To do this we used anterograde and retrograde tracers in conjunction with open source neuronal segmentation and tracer detection tools to generate whole brain connectivity maps of parietal inputs and outputs. Our present results show that inputs to the parietal cortex varied significantly along the medial-lateral, but not the rostral-caudal axis. Specifically, retrosplenial connectivity is greater medially, but connectivity with visual cortex, though generally sparse, is more significant laterally. Finally, based on connection density, the connectivity between parietal cortex and hippocampus is indirect and likely achieved largely via dysgranular retrosplenial cortex. Thus, similar to primates, the parietal cortex of rats exhibits a difference in connectivity along the medial-lateral axis, which may represent functionally distinct areas.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it