Locus coeruleus patterns differentially modulate learning and valence in rat <i>via</i> the ventral tegmental area and basolateral amygdala respectively
Bibliographic record
Abstract
ABSTRACT The locus coeruleus (LC), the main source of forebrain norepinephrine, produces phasic and tonic firing patterns that are theorized to have distinct functional consequences. However, how different firing modes affect learning and valence coding of sensory information are unknown. Here bilateral optogenetic activation of rat LC neurons using 10-Hz phasic trains of either 300 msec or 10 sec accelerates acquisition of a food-rewarded similar odor discrimination, but not a dissimilar odor discrimination, consistent with LC-supported enhanced pattern separation and plasticity. Similar odor discrimination learning is impaired by noradrenergic blockade in the piriform cortex (PC). However, here 10-Hz LC phasic light-mediated learning facilitation is prevented by a dopaminergic antagonist in the PC, or by ventral tegmental area (VTA) silencing with lidocaine, suggesting an LC-VTA-PC dopamine circuitry mediates 10-Hz phasic learning facilitation. Tonic stimulation at 10 Hz did not alter odor discrimination acquisition, and was less effective in activating VTA DA neurons. For valence encoding, tonic stimulation at 25 Hz induced freezing, anxiety and conditioned odor aversion, while 10-Hz phasic stimulation produced an odor preference consistent with positive valence. Noradrenergic blockade in the basolateral amygdala (BLA) prevented conditioned odor preference and aversion induced by 10-Hz phasic and 25-Hz tonic light respectively. CTB retro-labeling showed relatively larger engagement of nucleus accumbens projecting neurons over central amygdala projecting neurons in the BLA with 10-Hz LC phasic activation, compared to 25-Hz tonic. These outcomes argue that LC pauses, as well as LC firing frequencies, differentially influence both target networks and behaviour.
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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".