Primacy of vision shapes behavioral strategies and neural substrates of spatial navigation in the hippocampus of the common marmoset
Bibliographic record
Abstract
Abstract The mammalian hippocampus has been compared to a Global Positioning System (GPS) that enables spatial navigation. This notion has been primarily drawn from studies conducted in nocturnal mammals, such as rats; that lack many adaptations to daylight vision compared to diurnal primates. Here we demonstrate that during foraging in a 3D maze, the common marmoset, a new world diurnal primate with foveal, stereo-color vision, predominantly uses rapid head-gaze shifts to visually explore their surroundings while remaining stationary, and then minimizes head movements to navigate towards goals. On the other hand, rats, mainly move their head at low velocities while locomoting to explore the environment using their whiskers. These differences in exploration-navigation strategies reflect the species' sensory adaptations to different ecological niches. In the marmoset hippocampus CA3/CA1 regions putative pyramidal neurons show selectivity for 3D view, head direction, and less for place, but mainly mixed selectivity for combinations of these variables. Despite weak place selectivity, the spatial position of the animal in the maze can be decoded from the activity of small ensembles of mixed selective neurons. Inhibitory interneurons are tuned to 3D angular head velocity and translation speed, with most cells showing mixed selectivity for both variables. Finally, marmosets lack the rhythmic theta oscillations of local field potentials seen during locomotion in rats. Instead, they show resetting of theta oscillations triggered by head-gaze shifts that co-occurred with the activation of inhibitory interneurons, followed by various modulations in the activity of pyramidal cells. Our results show that the marmoset visual exploration/navigation strategies and the hippocampal neuronal specializations supporting them diverge from those observed in rats, reflecting the far-sensing capabilities of the marmoset visual system adapted to diurnal lifestyle.
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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".