The emergence of temporal hyperacuity from widely tuned cell populations
Bibliographic record
Abstract
Typically, individual neural cells operate on a millisecond time scale yet behaviorally animals reveal sub-microsecond acuity. Our model resolves this huge discrepancy by using populations of many widely tuned cells to attain sub-microsecond resolution in a temporal discrimination task. An echolocating bat uses its auditory system to locate objects and it demonstrates remarkable temporal precision in psychophysical tasks. Auditory cells were simulated using realistic parameters and connected in three ascending layers with descending projections from auditory cortex. Coincidence detection of firing collicular cells at thalamus and subsequent integration of multiple inputs at cortex, produce an estimate of time represented as the mean of the active cortical population. Multiple estimates allow the model bat to use memory to recognize predictable change in stimuli values. The best performance is produced using cortical feedback and a computation of target time based on combining the current and previous estimates. Temporal hyperacuity is attained through population coding of physiologically realistic cells but depends on the inherent properties of the psychophysical task.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".