Neural representation of sound amplitude by functionally different auditory receptors in crickets
Bibliographic record
Abstract
The physiological characteristics of auditory receptor fibers (ARFs) of crickets, a model system for studying auditory behaviors and their neural mechanisms, are investigated. Unlike auditory receptor neurons of many animals, cricket ARFs fall into three distinct populations based on characteristic frequency (CF) [Imaizumi and Pollack, J. Neurosci. 19, 1508-1516 (1999)]. Two of these have CFs similar to the frequency component of communication signals or of ultrasound produced by predators, and a third population has intermediate CF. Here, sound-amplitude coding by ARFs is examined to gain insights to how behaviorally relevant sounds are encoded by populations of receptor neurons. ARFs involved in acoustic communication comprise two distinct anatomical types, which also differ in physiological parameters (threshold, response slope, dynamic range, minimum latency, and sharpness of tuning). Thus, based on CF and anatomy, ARFs comprise four populations. Physiological parameters are diverse, but within each population they are systematically related to threshold. The details of these relationships differ among the four populations. These findings open the possibility that different ARF populations differ in functional organization.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".