Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Signal duration is important for identifying sound sources and determining signal meaning. Duration-tuned neurons (DTNs) respond preferentially to a range of stimulus durations and maximally to a best duration (BD). Duration-tuned neurons are found in the auditory midbrain of many vertebrates, although studied most extensively in bats. Studies of DTNs across vertebrates have identified cells with BDs and temporal response bandwidths that mirror the range of species-specific vocalizations. Neural tuning to stimulus duration appears to be universal among hearing vertebrates. Herein, we test the hypothesis that neural mechanisms underlying duration selectivity may be similar across vertebrates. We instantiated theoretical mechanisms of duration tuning in computational models to systematically explore the roles of excitatory and inhibitory receptor strengths, input latencies, and membrane time constant on duration tuning response profiles. We demonstrate that models of duration tuning with similar neural circuitry can be tuned with species-specific parameters to reproduce the responses of in vivo DTNs from the auditory midbrain. To relate and validate model output to in vivo responses, we collected electrophysiological data from the inferior colliculus of the awake big brown bat, Eptesicus fuscus, and present similar in vivo data from the published literature on DTNs in rats, mice, and frogs. Our results support the hypothesis that neural mechanisms of duration tuning may be shared across vertebrates despite species-specific differences in duration selectivity. Finally, we discuss how the underlying mechanisms of duration selectivity relate to other auditory feature detectors arising from the interaction of neural excitation and inhibition.
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.
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