A serotonergic recurrent inhibitory network filters threat information over behavioral timescales
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".