Increasing Spectrotemporal Sound Density Reveals an Octave-Based Organization in Cat Primary Auditory Cortex
Bibliographic record
Abstract
Auditory neurons are likely adapted to process complex stimuli, such as vocalizations, which contain spectrotemporal modulations. However, basic properties of auditory neurons are often derived from tone pips presented in isolation, which lack spectrotemporal modulations. In this context, it is unclear how to deduce the functional role of auditory neurons from their tone pip-derived tuning properties. In this study, spectrotemporal receptive fields (STRFs) were obtained from responses to multi-tone stimulus ensembles differing in their average spectrotemporal density (i.e., number of tone pips per second). STRFs for different stimulus densities were derived from multiple single-unit activity (MUA) and local field potentials (LFPs), simultaneously recorded in primary auditory cortex of cats. Consistent with earlier studies, we found that the spectral bandwidth was narrower for MUA compared with LFPs. Both neural firing rate and LFP amplitude were reduced when the density of the stimulus ensemble increased. Surprisingly, we found that increasing the spectrotemporal sound density revealed with increasing clarity an over-representation of response peaks at frequencies of approximately 3, 5, 10, and 20 kHz, in both MUA- and LFP-derived STRFs. Although the decrease in spectral bandwidth and neural activity with increasing stimulus density can likely be accounted for by forward suppression, the mechanisms underlying the over-representation of the octave-spaced response peaks are unclear. Plausibly, the over-representation may be a functional correlate of the periodic pattern of corticocortical connections observed along the tonotopic axis of cat auditory cortex.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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".