Spatial Information Encoding across Multiple Neocortical Regions Depends on an Intact Hippocampus
Bibliographic record
Abstract
There has been considerable research showing populations of neurons encoding for different aspects of space in the brain. Recently, several studies using two-photon calcium imaging and virtual navigation have identified “spatially” modulated neurons in the posterior cortex. We enquire here whether the presence of such spatial representations may be a cortex-wide phenomenon and, if so, whether these representations can be organized in the absence of the hippocampus. To this end, we imaged the dorsal cortex of mice running on a treadmill populated with tactile cues. A high percentage (40–80%) of the detected neurons exhibited sparse, spatially localized activity, with activity fields uniformly localized over the track. The development of this location specificity was impaired by hippocampal damage. Thus, there is a substantial population of neurons distributed widely over the cortex that collectively form a continuous representation of the explored environment, and hippocampal outflow is necessary to organize this phenomenon. SIGNIFICANCE STATEMENT Increasing evidence points to the role of the neocortex in encoding spatial information. Whether this feature is linked to hippocampal functions is largely unknown. Here, we systematically surveyed multiple regions in the dorsal cortex of the same animal for the presence of signals encoding for spatial position. We described populations of cortical neurons expressing sequential patterns of activity localized in space in primary, secondary, and associational areas. Furthermore, we showed that the formation of these spatial representations was impacted by hippocampal lesion. Our results indicate that hippocampal inputs are necessary to maintain a precise cortical representation of space.
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How this classification was reachedexpand
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 | 0.000 | 0.006 |
| 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 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".