Temporal Coding by Populations of Auditory Receptor Neurons
Bibliographic record
Abstract
Auditory receptor neurons of crickets are most sensitive to either low or high sound frequencies. Earlier work showed that the temporal coding properties of first-order auditory interneurons are matched to the temporal characteristics of natural low- and high-frequency stimuli (cricket songs and bat echolocation calls, respectively). We studied the temporal coding properties of receptor neurons and used modeling to investigate how activity within populations of low- and high-frequency receptors might contribute to the coding properties of interneurons. We confirm earlier findings that individual low-frequency-tuned receptors code stimulus temporal pattern poorly, but show that coding performance of a receptor population increases markedly with population size, due in part to low redundancy among the spike trains of different receptors. By contrast, individual high-frequency-tuned receptors code a stimulus temporal pattern fairly well and, because their spike trains are redundant, there is only a slight increase in coding performance with population size. The coding properties of low- and high-frequency receptor populations resemble those of interneurons in response to low- and high-frequency stimuli, suggesting that coding at the interneuron level is partly determined by the nature and organization of afferent input. Consistent with this, the sound-frequency-specific coding properties of an interneuron, previously demonstrated by analyzing its spike train, are also apparent in the subthreshold fluctuations in membrane potential that are generated by synaptic input from receptor neurons.
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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 | 0.000 |
| 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".