Sensory Habituation of Auditory Receptor Neurons: Implications for Sound Localization
Bibliographic record
Abstract
Auditory receptor neurons exhibit sensory habituation; their responses decline with repeated stimulation. We studied the effects of sensory habituation on the neural encoding of sound localization cues using crickets as a model system. In crickets, Teleogryllus oceanicus, sound localization is based on binaural comparison of stimulus intensity. There are two potential codes at the receptor-neuron level for interaural intensity difference: interaural difference in response strength, i.e. spike rate and/or count, and interaural difference in response latency. These are affected differently by sensory habituation. When crickets are stimulated with cricket-song-like trains of sound pulses, response strength declines for successive pulses in the train, and the decrease becomes more pronounced as the stimulus intensity increases. Response decrement is thus greater for receptors serving the ear ipsilateral to the sound source, where intensity is higher, resulting in a decrease in the interaural difference in response strength. Sensory habituation also affects response latency, which increases for responses to successive sound pulses in the stimulus train. The change in latency is independent of intensity, and thus is similar for receptors serving both ears. As a result, interaural latency difference is unaffected by sensory habituation and may be a more reliable cue for sound localization.
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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 | 0.001 |
| 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".