A serotonergic recurrent inhibitory network filters threat information over behavioral timescales
Why this work is in the frame
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Bibliographic record
Abstract
*The habenulo-raphe pathway is implicated in orchestrating optimal behavioral responses to aversive, threatening or stressful environments. Here, we consider how long-range inputs from lateral habenula (LHb) influence circuit dynamics in the dorsal raphe nucleus (DRN). We find that habenulo-raphe afferents triggered classical monosynaptic excitation of 5-HT neurons, as well as strong disynaptic inhibition whose induction was steeply frequency-dependent and which persisted for seconds. This novel inhibition was mediated by a GIRK conductance activated by 5-HT1A receptors. Optogenetic and pharmacological manipulations in DRN revealed, unexpectedly, that 5-HT neurons are organized in a recurrent inhibitory network, refuting the classical model of autocrine activation of 5-HT1A autoreceptors. Electrical stimulation approaches revealed that these inhibitory connections exhibited robust, dramatic short-term facilitation that we formalized with a linear-nonlinear plasticity model. Using experimentally-constrained network models, we found that excitatory inputs led to paradoxical serotonergic inhibition at high frequencies, and this polarity switch was dependent on plasticity dynamics and not on recurrent inhibition itself. To test the physiological relevance of this computation for processing threat information from the LHb, we developed a simple auditory classical conditioning paradigm and tested key predictions of our model through in vivo optogenetics. Notably, stimulating the habenulo-raphe pathway at high frequencies, but not at low frequencies, depressed goal-directed anticipatory licking behavior. We suggest that the computation sustained by this circuit motif categorizes synaptic inputs to implement optimal adaption of behavioural policies in threatening environments.
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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 | 0.000 | 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 | 0.000 |
| 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 it