Enriched experience increases reciprocal synaptic connectivity and coding sparsity in higher-order cortex
Bibliographic record
Abstract
The integration of new information during sleep reshapes cortical representations that support categorical knowledge. Auto-associative attractor network theories predict that reciprocal excitatory connections help form stable categorical attractors, but direct evidence is missing. We tested this using ten weeks of enriched experience (ENR) in mice as a model for knowledge accumulation and recorded single-unit activity across hippocampus and neocortex. ENR induced significant remodeling in high- but not low-level neocortex, with a shift from unidirectional to bidirectional excitatory-excitatory connections, suggestive of increased 'cell assemblies'. This was accompanied by increased inhibitory-to-excitatory connections and sparser, more orthogonal population activity during awake rest and slow-wave sleep, particularly in deep layers. Thus, ENR reorganizes cortical circuits into a symmetric, inhibition-balanced network that improves coding efficiency, supporting long-standing attractor network predictions.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".