Locus Coeruleus Activation Patterns Differentially Modulate Odor Discrimination Learning and Odor Valence in Rats
Bibliographic record
Abstract
The locus coeruleus (LC) produces phasic and tonic firing patterns that are theorized to have distinct functional consequences. However, how different firing modes affect learning and valence encoding of sensory information are unknown. Here, we show bilateral optogenetic activation of rat LC neurons using 10-Hz phasic trains of either 300 ms or 10 s accelerated acquisition of a similar odor discrimination. Similar odor discrimination learning was impaired by noradrenergic blockade in the piriform cortex (PC). However, 10-Hz phasic light-mediated learning facilitation was prevented by a dopaminergic antagonist in the PC, or by ventral tegmental area (VTA) silencing with lidocaine, suggesting a LC-VTA-PC dopamine circuitry involvement. Ten-hertz tonic stimulation did not alter odor discrimination acquisition, and was ineffective in activating VTA DA neurons. For valence encoding, tonic stimulation at 25 Hz induced conditioned odor aversion, whereas 10-Hz phasic stimulations produced an odor preference. Both conditionings were prevented by noradrenergic blockade in the basolateral amygdala (BLA). Cholera Toxin B retro-labeling showed larger engagement of nucleus accumbens-projecting neurons in the BLA with 10-Hz phasic activation, and larger engagement of central amygdala projecting cells with 25-Hz tonic light. These outcomes argue that the LC activation patterns differentially influence both target networks and 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 | 0.001 |
| 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".