Running promotes spatial bias independently of adult neurogenesis
Bibliographic record
Abstract
Different memory systems offer distinct advantages to navigational behavior. The hippocampus forms complex associations between environmental stimuli, enabling flexible navigation through space. In contrast, the dorsal striatum associates discrete cues and favorable behavioral responses, enabling habit-like, automated navigation. While these two systems often complement one another, there are instances where striatal-dependent responses (e.g. approach a cue) conflict with hippocampal representations of spatial goals. In conflict situations, preference for spatial vs. response strategies varies across individuals and depends on previous experience, plasticity and the integrity of these two memory systems. Here, we investigated the role of adult hippocampal neurogenesis and exercise on mouse search strategies in a water maze task that can be solved with either a hippocampal-dependent place strategy or a striatal-dependent cue-response strategy. We predicted that inhibiting adult neurogenesis would impair hippocampal function and shift behavior towards striatal-dependent cue responses. However, blocking neurogenesis in a transgenic nestin-TK mouse did not affect strategy choice. We then investigated whether a pro-neurogenic stimulus, running, would bias mice towards hippocampal-dependent spatial strategies. While running indeed promoted spatial strategies, it did so even when neurogenesis was inhibited in nestin-TK mice. These findings indicate that exercise-induced increases in neurogenesis are not always required for enhanced cognitive function. Furthermore, our data identify exercise as a potentially useful strategy for promoting flexible, cognitive forms of memory in habit-related disorders that are characterized by excessive responding to discrete cues.
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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 | 0.001 |
| 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".