Learning-related coordination of striatal and hippocampal theta rhythms during acquisition of a procedural maze task
Bibliographic record
Abstract
The striatum and hippocampus are conventionally viewed as complementary learning and memory systems, with the hippocampus specialized for fact-based episodic memory and the striatum for procedural learning and memory. Here we directly tested whether these two systems exhibit independent or coordinated activity patterns during procedural learning. We trained rats on a conditional T-maze task requiring navigational and cue-based associative learning. We recorded local field potential (LFP) activity with tetrodes chronically implanted in the caudoputamen and the CA1 field of the dorsal hippocampus during 6-25 days of training. We show that simultaneously recorded striatal and hippocampal theta rhythms are modulated differently as the rats learned to perform the T-maze task but nevertheless become highly coherent during the choice period of the maze runs in rats that successfully learned the task. Moreover, in the rats that acquired the task, the phase of the striatal-hippocampal theta coherence was modified toward a consistent antiphase relationship, and these changes occurred in proportion to the levels of learning achieved. We suggest that rhythmic oscillations, including theta-band activity, could influence not only neural processing in cortico-basal ganglia circuits but also dynamic interactions between basal ganglia-based and hippocampus-based forebrain circuits during the acquisition and performance of learned behaviors. Experience-dependent changes in coordination of oscillatory activity across brain structures thus may parallel the well known plasticity of spike activity that occurs as a function of experience.
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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".