Computational Models of Millisecond Level Duration Tuning in Neural Circuits
Bibliographic record
Abstract
Discrimination of stimulus duration on the order of milliseconds has been observed in behavioral and neurophysiological studies across a variety of species and taxa. Several studies conducted in mammals have found neurons in the auditory midbrain (inferior colliculus) that are selective for signal duration. Duration selectivity in these cells arises from an interaction of excitatory and inhibitory events occurring at particular latencies from stimulus onset and offset. As previously shown in barn owls, coincidence of delayed, excitatory events can be used by the CNS to respond selectively to specific stimuli in auditory space. This study formulates several computational models of duration tuning that combine existing conceptual models with observed physiological responses in the auditory brainstem and midbrain to evaluate the plausibility of the proposed neural mechanisms. The computational models are able to reproduce a wide range of in vivo responses including best duration tuning, duration-selective response classes, spike counts, first-spike latencies, level tolerance to changes in signal amplitude, and neuropharmacological effects of applying inhibitory neurotransmitter antagonists to duration-tuned neurons. A unified model of duration tuning is proposed that enhances classic models of duration tuning, emphasizes similarities across the models, and simplifies our understanding of duration tuning across species and sensory modalities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".