Developmental Differences in Hippocampal EEG During REM Sleep: A Chaotic and Spectral Analysis
Bibliographic record
Abstract
This study examines developmental differences in the hippocampal electroencephalogram (EEG) during rapid eye movement (REM) sleep in rats, with a focus on characterizing the underlying nonlinear dynamics using an RMS-normalized modified Grassberger-Procaccia (G-P) algorithm. EEG signals were recorded from two key hippocampal subfields, the CA1 region and the dentate gyrus (DG), in freely moving animals at two postnatal stages: 15 and 90 days of age. To quantify the complexity of the EEG, we employed time-delay embedding and calculated the correlation dimension via a modified GP method. The reconstructed phase space trajectories revealed age-dependent differences in the dimensional complexity of the signals. Specifically, younger animals exhibited significantly greater correlation dimension values in the DG relative to CA1, a difference that was no longer present in older animals. These findings suggest that the maturation of hippocampal circuits during early development is accompanied by changes in the structure of chaotic neural activity during REM sleep. Methodologically, we apply an RMS-normalized variant of the Grassberger-Procaccia algorithm to stabilize$D_{2}$across embeddings, enabling the first subfield-by-age comparison of nonlinear REM EEG dynamics in early development. Our results underscore the utility of chaotic signal analysis in probing subtle developmental dynamics not readily captured by conventional linear techniques.
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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) | 0.000 | 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 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".