Bibliographic record
Abstract
It is becoming increasingly clear that the brain processes sensory stimuli differently according to whether they are passively or actively acquired, and these differences can be seen early in the sensory pathway. In the nucleus of the solitary tract (NTS), the first relay in the central gustatory neuraxis, a rich variety of sensory inputs generated by active licking converge. Here, we show that taste responses in the NTS reflect these interactions. Experiments consisted of recordings of taste-related activity in the NTS of awake rats as they freely licked exemplars of the five basic taste qualities (sweet, sour, salty, bitter, umami). Nearly all taste-responsive cells were broadly tuned across taste qualities. A subset responded to taste with long latencies (>1.0 s), suggesting the activation of extraoral chemoreceptors. Analyses of the temporal characteristics of taste responses showed that spike timing conveyed significantly more information than spike count alone in almost one-half of NTS cells, as in anesthetized rats, but with less information per cell. In addition to taste-responsive cells, the NTS contains cells that synchronize with licks. Since the lick pattern per se can convey information, these cells may collaborate with taste-responsive cells to identify taste quality. Other cells become silent during licking. These latter “antilick” cells show a surge in firing rate predicting the beginning and signaling the end of a lick bout. Collectively, the data reveal a complex array of cell types in the NTS, only a portion of which include taste-responsive cells, which work together to acquire sensory information.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.116 | 0.053 |
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".