Serotonin modulates optimized coding of natural stimuli through increased neural and behavioural responses via enhanced burst firing
Bibliographic record
Abstract
KEY POINTS: The function of serotonergic fibres onto sensory areas remains poorly understood We show that serotonin application enhances sensory neural and behavioural responses to second order stimuli Enhanced neural responses most likely occurred because of increased burst firing Changes in neural sensitivity due to burst firing were the best predictor of changes in behavioural sensitivity Our results suggest that serotonin optimizes coding of stimuli encountered during aggression. ABSTRACT: Understanding how the processing of sensory information leads to behavioural responses remains a central problem in systems neuroscience. Here, we investigated how the neuromodulator serotonin affects neural and behavioural responses to second-order envelope stimuli within the electrosensory system of the weakly electric fish Apteronotus leptorhynchus. We found that serotonin application increased neuronal excitability through greater tendency for burst firing. We found that increased excitability led to overall higher neural sensitivities to higher envelope frequencies. Separating the spike train into bursts and isolated spike train components revealed that this was due to significant increases in neural sensitivity for the former but not the latter. We next investigated the consequences of such changes in sensitivity towards optimized coding of stimuli with specific statistics. Our results show that serotonin application compromised optimal coding of stimuli with statistics seen under naturalistic conditions due to changes in burst, but not isolated spike firing. Finally, we found that serotonin application increased behavioural sensitivity to envelope stimuli. Interestingly, changes in neural sensitivity due to bursts were a far better predictor of changes in behavioural sensitivity, suggesting that burst firing is decoded by downstream brain areas. Overall, our results suggest that serotonin modulates neural responses to optimize coding and perception of stimuli during behavioural contexts associated with encountering dominant conspecifics.
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".