Organization and trade-off of spectro-temporal tuning properties of duration-tuned neurons in the mammalian inferior colliculus
Bibliographic record
Abstract
Neurons throughout the mammalian central auditory pathway respond selectively to stimulus frequency and amplitude, and some are also selective for stimulus duration. First found in the auditory midbrain or inferior colliculus (IC), these duration-tuned neurons (DTNs) provide a potential neural mechanism for encoding temporal features of sound. In this study, we investigated how having an additional neural response filter, one selective to the duration of an auditory stimulus, influences frequency tuning and neural organization by recording single-unit responses and measuring the dorsal-ventral position and spectral-temporal tuning properties of auditory DTNs from the IC of the awake big brown bat (Eptesicus fuscus). Like other IC neurons, DTNs were tonotopically organized and had either V-shaped, U-shaped, or O-shaped frequency tuning curves (excitatory frequency response areas). We hypothesized there would be an interaction between frequency and duration tuning in DTNs, as electrical engineering theory for resonant filters dictates a trade-off in spectral-temporal resolution: sharp tuning in the frequency domain results in poorer resolution in the time domain and vice versa. While the IC is a more complex signal analyzer than an electrical filter, a similar operational trade-off could exist in the responses of DTNs. Our data revealed two patterns of spectro-temporal sensitivity and spatial organization within the IC: DTNs with sharp frequency tuning and broad duration tuning were located in the dorsal IC, whereas cells with wide spectral tuning and narrow temporal tuning were found in the ventral IC.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".