Ventral Striatal Dopamine Increases following Hippocampal Sharp-Wave Ripples
Bibliographic record
Abstract
Summary The reactivation of task-related hippocampal activity during sharp-wave ripples (SWRs), often expressed as sequential “replay,” is thought to contribute to behavior through both online and offline processes. During sleep and awake rest, replay supports memory consolidation, broadly defined as the updating and stabilization of knowledge structures for later use. During active task engagement, replay may also support decision-making through episodic memory retrieval and the generation of prospective scenarios 1–4 (but see 5 ). While disrupting SWR events impairs memory-guided behavior 6–9 and augmenting them enhances such behavior 10,11 , the neurophysiological mechanisms that enable replay to contribute to behavior remain unclear. Theories of replay-based learning posit the need for replay events to be followed by an evaluative signal. Dopamine (DA) is consistently associated with such teaching signals, particularly in the ventral striatum, where it reflects reward- and other prediction-error signals 12 . Although hippocampal activity can influence mesolimbic dopamine signaling 13–15 , whether SWRs are coupled to ventral striatal DA release is unknown. To address this, we simultaneously recorded dorsal CA1 local field potentials to detect SWRs and measured ventral striatal DA concentration using fiber photometry of the GRAB DA2m sensor 16 in freely moving mice. We found a significant increase in DA following SWRs. This coupling was more prominent during offline rest than during periods of immobility during behavior, and was stronger following longer and higher-power SWRs. These results reveal coupling between hippocampal SWRs and striatal dopamine, providing a potential candidate mechanism through which internally generated hippocampal activity could engage evaluative signals during offline memory processing.
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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 | 0.001 |
| 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".