Bibliographic record
Abstract
The generation of new neurons in the adult mammalian brain has led to numerous theories as to their functional significance. One of the most widely held views is that adult neurogenesis promotes pattern separation, a process by which overlapping patterns of neural activation are mapped to less overlapping representations. While a large body of evidence supports a role for neurogenesis in high interference memory tasks, it does not support the proposed function of neurogenesis in mediating pattern separation. Instead, the adult-generated neurons seem to generate highly overlapping and yet distinct distributed representations for similar events. One way in which these immature, highly plastic, hyperactive neurons may contribute to novel memory formation while avoiding interference is by virtue of their extremely sparse connectivity with incoming perforant path fibers. Another intriguing proposal, awaiting empirical confirmation, is that the young neurons' recruitment into memory formation is gated by a novelty/mismatch mechanism mediated by CA3 or hilar back-projections. Ongoing research into the intriguing link between neurogenesis, stress-related mood disorders, and age-related neurodegeneration may lead to promising neurogenesis-based treatments for this wide range of clinical disorders. WIREs Cogn Sci 2017, 8:e1427. doi: 10.1002/wcs.1427 For further resources related to this article, please visit the WIREs website.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".