Responses of neurons in the rat’s inferior colliculus to a sound are affected by another sound in a space-dependent manner
Bibliographic record
Abstract
The perception of a sound can be influenced by another sound in a space-dependent manner. An understanding of this perceptual phenomenon depends on knowledge about how the spatial relationship between two sounds affects neural responses to the sounds. We used the rat as a model system and equal-probability two-tone sequences as stimuli to evaluate how spatial separation between two asynchronously recurring sounds affected responses to the sounds in midbrain auditory neurons. We found that responses elicited by two tone bursts when they were colocalized at the ear contralateral to the neuron were different from the responses elicited by the same sounds when they were separated with one at the contralateral ear while the other at another location. For neurons with transient sound-driven firing and not responsive to stimulation presented at the ipsilateral ear, the response to a sound with a fixed location at the contralateral ear was enhanced when the second sound was separated. These neurons were likely important for detecting a sound in the presence of a spatially separated competing sound. Our results suggest that mechanisms underlying effects of spatial separation on neural responses to sounds may include adaptation and long-lasting binaural excitatory/inhibitory interaction.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".