Tuning for rate and duration of frequency-modulated sweeps in the mammalian inferior colliculus
Bibliographic record
Abstract
Responses of auditory duration-tuned neurons (DTNs) are selective for stimulus duration. We used single-unit extracellular recording to investigate how the inferior colliculus (IC) encodes frequency-modulated (FM) sweeps in the big brown bat. It was unclear whether the responses of so-called "FM DTNs" encode signal duration, like classic pure-tone DTNs, or the FM sweep rate. Most FM cells had spiking responses selective for downward FM sweeps. We presented cells with linear FM sweeps whose center frequency (CEF) was set to the best excitatory frequency and whose bandwidth (BW) maximized the spike count. With these baseline parameters, we stimulated cells with linear FM sweeps randomly varied in duration to measure the range of excitatory FM durations and/or sweep rates. To separate FM rate and FM duration tuning, we doubled (and halved) the BW of the baseline FM stimulus while keeping the CEF constant and then recollected each cell's FM duration tuning curve. If the cell was tuned to FM duration, then the best duration (or range of excitatory durations) should remain constant despite changes in signal BW; however, if the cell was tuned to the FM rate, then the best duration should covary with the same FM rate at each BW. A Bayesian model comparison revealed that the majority of neurons were tuned to the FM sweep rate, although a few cells showed tuning for FM duration. We conclude that the dominant parameter for temporal tuning of FM neurons in the IC is FM sweep rate and not FM duration. NEW & NOTEWORTHY Reports of inferior colliculus neurons with response selectivity to the duration of frequency-modulated (FM) stimuli exist, yet it remains unclear whether such cells are tuned to the FM duration or the FM sweep rate. To disambiguate these hypotheses, we presented neurons with variable-duration FM signals that were systematically manipulated in bandwidth. A Bayesian model comparison revealed that most temporally selective midbrain cells were tuned to the FM sweep rate and not the FM duration.
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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 | 0.002 |
| 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 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".