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 α 1 and β-adrenoceptors, whereas tonic LC activation induced-negative odor valence learning depends on β-adrenoceptors only. In parallel, both α 1 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 α 1 + 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".