Decoding stimulus duration from neural responses in the auditory midbrain
Bibliographic record
Abstract
Neurons with responses selective for the duration of an auditory stimulus are called duration-tuned neurons (DTNs). Temporal specificity in their spiking suggests that one function of DTNs is to encode stimulus duration; however, the efficacy of duration encoding by DTNs has yet to be investigated. Herein, we characterize the information content of individual cells and a population of DTNs from the mammalian inferior colliculus (IC) by measuring the stimulus-specific information (SSI) and estimated Fisher information (FI) of spike count responses. We found that SSI was typically greatest for those stimulus durations that evoked maximum spike counts, defined as best duration (BD) stimuli, and that FI was maximal for stimulus durations off BD where sensitivity to a change in duration was greatest. Using population data, we demonstrate that a maximum likelihood estimator (MLE) can accurately decode stimulus duration from evoked spike counts. We also simulated a two-alternative forced choice task by having MLE models decide whether two durations were the same or different. With this task we measured the just-noticeable difference threshold for stimulus duration and calculated the corresponding Weber fractions across the stimulus domain. Altogether, these results demonstrate that the spiking responses of DTNs from the mammalian IC contain sufficient information for the CNS to encode, decode, and discriminate behaviorally relevant auditory signal durations.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| 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".