Optical activation of midbrain dopamine neurons: do high and low stimulation frequencies produce functionally different effects?
Bibliographic record
Abstract
Millard et al. propose 1 that 20- and 50-Hz stimulation of optically excitable, midbrain dopamine neurons triggers functionally distinct effects: The “physiological” firing elicited by a 20-Hz train mimics a reward-prediction error (RPE) whereas the “supra-physiological” firing induced by a 50-Hz train creates “a sensory event that acts as a reward in its own right.” Only the 50-Hz trains supported vigorous responding in the face of elevated response costs and sufficed to produce reward-specific Pavlovian-Instrumental Transfer (PIT). Nonetheless, the 20-Hz trains did produce unblocking. Here, we propose a more parsimonious account of these findings that is consistent with the results of prior experiments on operant responding for rewarding optical 2–4 or electrical 5–9 stimulation and with a new theory 10,11 of the role of dopamine signaling in associative conditioning: 1) The effects of the 20- and 50-Hz trains differ quantitatively rather than qualitatively. They are mapped onto a single dimension, reward intensity, which reflects the aggregate release of dopamine in the critical terminal field(s). 2) The non-linear dependence of operant responding on both the aggregate dopamine release and the response cost 3 can explain why responding for the 20- and 50 Hz trains diverged as response cost grew and predicts that this divergence can be eliminated by increasing the number of dopamine neurons stimulated. 3) Terminal-field dopamine concentration provides no information about pulse frequency per se. 4) Operant responding, PIT, and unblocking all depend on reward intensity, but the form and parameters of this dependence can differ, thus generating the observed contrasts in the impact of the 20- and 50-Hz trains. 5) The tested range of experimental conditions is too narrow to specify what constitutes “physiological” firing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".