Dopamine neurons drive spatiotemporally heterogeneous striatal dopamine signals during learning
Bibliographic record
Abstract
Environmental cues, through Pavlovian learning, become conditioned stimuli that invigorate and guide animals toward acquisition of rewards. Dopamine neurons in the ventral tegmental area (VTA) and substantia nigra (SNC) are crucial for this process. Dopamine neurons are embedded in a reciprocally connected network with their striatal targets, the functional organization of which remains poorly understood. Here, we investigated how learning during optogenetic Pavlovian cue conditioning of VTA or SNC dopamine neurons directs cue-evoked behavior and shapes subregion-specific striatal dopamine dynamics. We used a fluorescent dopamine biosensor to monitor dopamine in the nucleus accumbens (NAc) core and shell, dorsomedial striatum (DMS), and dorsolateral striatum (DLS). We demonstrate spatially heterogeneous, learning-dependent dopamine changes across striatal regions. While VTA stimulation evoked robust dopamine release in NAc core, shell, and DMS, cues predictive of this activation preferentially recruited dopamine release in NAc core, starting early in training, and DMS, late in training. Corresponding negative prediction error signals, reflecting a violation in the expectation of dopamine neuron activation, only emerged in the NAc core and DMS, and not the shell. Despite development of vigorous movement late in training, conditioned dopamine signals did not similarly emerge in the DLS, even during Pavlovian conditioning with SNC dopamine neuron activation, which elicited robust DLS dopamine release. Together, our studies show broad dissociation in the fundamental prediction and reward-related information generated by different dopamine neuron populations and signaled by dopamine across the striatum. Further, they offer new insight into how larger-scale plasticity across the striatal network emerges during Pavlovian learning to coordinate behavior.
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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".