Increased Entorhinal–Prefrontal Theta Synchronization Parallels Decreased Entorhinal–Hippocampal Theta Synchronization during Learning and Consolidation of Associative Memory
Bibliographic record
Abstract
Memories are thought to be encoded as a distributed representation in the neocortex. The medial prefrontal cortex (mPFC) has been shown to support the expression of memories that initially depend on the hippocampus (HPC), yet the mechanisms by which the HPC and mPFC access the distributed representations in the neocortex are unknown. By measuring phase synchronization of local field potential (LFP) oscillations, we found that learning initiated changes in neuronal communication of the HPC and mPFC with the lateral entorhinal cortex (LEC), an area that is connected with many other neocortical regions. LFPs were recorded simultaneously from the three brain regions while rats formed an association between an auditory stimulus (CS) and eyelid stimulation (US) in a trace eyeblink conditioning paradigm, as well as during retention 1 month following learning. Over the course of learning, theta oscillations in the LEC and mPFC became strongly synchronized following presentation of the CS on trials in which rats exhibited a conditioned response (CR), and this strengthened synchronization was also observed during remote retention. In contrast, CS-evoked theta synchronization between the LEC and HPC decreased with learning. Our results suggest that communication between the LEC and mPFC are strengthened with learning whereas the communication between the LEC and HPC are concomitantly weakened, suggesting that enhanced LEC-mPFC communication may be a neuronal correlate for theoretically proposed neocortical reorganization accompanying encoding and consolidation of a memory.
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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) | 0.001 | 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".