Adult-born neurons inhibit developmentally-born neurons in the dentate gyrus
Bibliographic record
Abstract
Abstract During hippocampal-dependent memory formation, sensory signals from the neocortex converge in the dentate gyrus. It is generally believed that the dentate gyrus decorrelates inputs in order to minimize interference between codes for similar experiences, often referred to as pattern separation. Emerging evidence from mouse models suggests that adult-born neurons exert an inhibitory influence on the dentate gyrus, which may be important for maintaining the sparse code that is needed to form precise memories. However, since the dentate gyrus is composed of a heterogeneous population of cells that are born throughout life, it is unclear if newborn neurons inhibit all cells equally. We therefore investigated whether adult neurogenesis in rats modulates activity in dentate gyrus neurons that are born at the peak of early postnatal development. Adult neurogenesis was increased by subjecting rats to an alternating running and memantine treatment schedule, and it was decreased with a transgenic GFAP-TK rat model. Activity was measured by quantifying experience-induced Fos expression in BrdU + cells. Consistent with an inhibitory role, enhancing neurogenesis blocked experience-dependent Fos expression in developmentally-born neurons and also in the broader granule cell population. In contrast, blocking neurogenesis did not significantly impact activity patterns. These results confirm previous work in mice and identify the developmentally-born population of neurons as a major target of neurogenesis-mediated inhibition. Treatments that target neurogenesis may therefore benefit disorders that are characterized by excitation-inhibition imbalance in the hippocampus, such as age-related memory impairments, fear and anxiety, and epilepsy.
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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".