Unraveling the neural correlates of dream recall: Novel insights from deep convolutional nets
Bibliographic record
Abstract
Dreams and our ability to recall them are among the most puzzling questions in sleep research. Specifically, putative differences in brain network dynamics between individuals with high versus low dream recall rates, are still poorly understood. In this study, we addressed this question as a classification problem where we applied deep convolutional networks (CNN) to sleep EEG recordings to predict whether subjects belonged to the high or low dream recall group (HDR and LDR resp.). More specifically, EEG was recorded during full-night sleep from 36 subjects (18 HDR and 18 LDR). We used a deep learning framework to discover features from the EEG data that are different between the two groups (as opposed to prior knowledge-driven hand-crafted feature analysis). For each sleep stage, we trained a separate CNN to classify EEG segments into HDR and LDR group. The classification accuracies indicated which sleep stages contained the best neural predictors of dream recall rate. While training the CNN, we added a subject discriminator network and used it as adversary to restrict the CNN from learning subject-specific features and instead learn features that are subject-agnostic but allow discrimination between HDR and LDR groups. These features were subsequently visualized using a method known as cue-combination for Class Activation Map (ccCAM), inspired by [1]. These findings are compared to results obtained in previous studies [2,3] and future research directions are discussed.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".