Environmental enrichment accelerates cortical memory consolidation
Bibliographic record
Abstract
Environmental enrichment is an established strategy to enhance learning and to build resilience against neurodegeneration. In humans, this is known as 'cognitive reserve'. Though the beneficial effects of exposure to a complex environment in animal models have been well-documented by behavioural, immunohistological and morphological observations, its impact on the functional properties of neuronal populations remains poorly understood. This study aimed to compare the functional encoding and offline memory retrieval dynamics of cortical neurons in enriched and control mice performing a virtual spatial foraging task. Thy1-GCaMP6s mice aged 21 days were enriched for 9 weeks by running a complex obstacle course, during which they were gradually exposed to many different types of obstacles requiring climbing, jumping, and/or balancing elements. Control animals were exercise-matched by running a similar track containing only repeating ramps. At the age of 3 months, two-photon calcium imaging was conducted on populations of neurons from the secondary motor cortex in both groups before, during, and after repeated locomotion through a virtual environment with salient visual-tactile cues. We observed an increase in memory reactivation in the enriched group during the first day of exposure. With training, enriched animals exhibited a stronger anticipatory reduction in running speed near the reward location. Moreover, cortical neuron activity representing locations on the track became substantially more stable and precise over days in enriched but not control animals. Altogether, these results indicate that prior environmental enrichment accelerates the consolidation of stable and task-relevant memory representations in the cortex for a novel task, and enables faster and more robust acquisition of new sequence representations.
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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) | 0.001 | 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".