Glutamate‐Dopamine Cotransmission and Reward Processing in Addiction
Bibliographic record
Abstract
While Dale's principle of "one neuron, one neurotransmitter" has undergone revisions to incorporate evidence of the corelease of atypical neurotransmitters such as neuropeptides, the corelease of classical neurotransmitters has only recently been realized. Surprisingly, numerous studies now indicate that the corelease of neurotransmitters in the mammalian central nervous system is not an obscure and rare phenomenon but is widespread and involves most classical neurotransmitters systems. However, the suggestion that glutamate can be coreleased with dopamine (DA) has remained controversial. Furthermore, glutamate-DA cotransmission has not yet been seriously considered in the context of the neurocircuitry of addiction. If glutamate is in fact coreleased with DA as some evidence now suggests, this may have significant implications for advancing our understanding of the interactive role that these 2 neurotransmitters play in cognitive and reward processes. In this commentary, we review the evidence for and against glutamate as a cotransmitter and discuss the potential role of glutamate-DA corelease in addiction. In particular, we describe a recently proposed model in which coreleased glutamate transmits a temporally precise prediction error signal of reward described by Schultz et al., whereas the function of coreleased DA is to exert prolonged modulatory influences on neuronal activity. In addition, we suggest that as alcohol consumption transitions from recreational use to addiction, there is a corresponding transition in the reward valence signal from better than predicted to worse than predicted.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".