Phasic and Tonic Locus Coeruleus Stimulation Associated Valence Learning Engages Distinct Adrenoceptors in the Rat Basolateral Amygdala
Bibliographic record
Abstract
Reward exploitation and aversion are mediated in part by the locus coeruleus (LC), a brainstem structure significantly involved in learning and memory via the release of norepinephrine. Different LC firing patterns are associated with different functions. Previously, we have shown that high tonic and phasic LC activation signal negative and positive valence, respectively, via basolateral amygdala (BLA) circuitry. Tonic LC activation is associated preferentially with BLA-central amygdala (CeA) activation, while phasic LC stimulation preferentially recruits the BLA-nucleus accumbens (NAc) pathway. Here, we ask if phasic and tonic LC activation-associated valence learning requires different adrenoceptors in the BLA, in comparison with the odor valence learning induced by natural reward and aversive conditioning. Using optogenetic activation of the LC and local drug infusions in the BLA, we show that phasic LC activation-induced positive odor valence learning is dependent on both α1 and β-adrenoceptors, whereas tonic LC activation induced-negative odor valence learning depends on β-adrenoceptors only. In parallel, both α1 and β-adrenoceptors were required in the odor valence learning induced by reward while aversive conditioning was dependent on β-adrenoceptors. Phasic stimulation and reward conditioning likewise activated more NAc-projectors of the BLA, in comparison to tonic and aversive conditioning. There was a higher proportion of α1+ cells in the NAc-projectors compared to CeA-projectors in the BLA. Together, these results provide insight into the mechanisms underlying the effects of tonic and phasic activation of the LC, and more generally, negative and positive valence signaling.
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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) | 0.001 | 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".