Multiplexed encoding of frequency-modulated sweep features in the inferior colliculus
Bibliographic record
Abstract
Within the central auditory pathway, the inferior colliculus (IC) is a critical integration center for ascending sound information. Previous studies have shown that many IC neurons exhibit receptive fields for individual features of auditory stimuli, such as sound frequency, intensity, and location, but growing evidence suggests that some IC neurons may multiplex features of sound. Here, we used in vivo juxtacellular recordings in awake, head-fixed mice to examine how IC neurons responded to frequency-modulated sweeps that varied in speed, direction, intensity, and frequency range. We then applied machine learning methods to determine how individual IC neurons encode features of FM sweeps. We found that individual IC neurons multiplex FM sweep features using various strategies including spike timing, distribution of inter-spike intervals, and first spike latency. In addition, we found that decoding accuracy for sweep direction can vary with sweep speed and frequency range, suggesting the presence of mixed selectivity in single neurons. Accordingly, using static receptive fields for direction alone yielded poor predictions of neuron responses to vocalizations that contain simple frequency changes. Lastly, we showed that encoding strategies varied across individual neurons, resulting in a highly informative population response for FM sweep features. Together, our results suggest that multiplexing sound features is a common mechanism used by IC neurons to represent complex sounds.
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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".