Sound-Induced Synchronization of Neural Activity Between and Within Three Auditory Cortical Areas
Bibliographic record
Abstract
Neural synchrony within and between auditory cortical fields is evaluated with respect to its potential role in feature binding and in the coding of tone and noise sound pressure level. Simultaneous recordings were made in 24 cats with either two electrodes in primary auditory cortex (AI) and one in anterior auditory field (AAF) or one electrode each in AI, AAF, and secondary auditory cortex. Cross-correlograms (CCHs) for 1-ms binwidth were calculated for tone pips, noise bursts, and silence (i.e., poststimulus) as a function of intensity level. Across stimuli and intensity levels the total percentage of significant stimulus onset CCHs was 62% and that of significant poststimulus CCHs was 58% of 1,868 pairs calculated for each condition. The cross-correlation coefficient to stimulus onsets was higher for single-electrode pairs than for dual-electrode pairs and higher for noise bursts compared with tone pips. The onset correlation for single-electrode pairs was only marginally larger than the poststimulus correlation. For pairs from electrodes across area boundaries, the onset correlations were a factor 3-4 higher than the poststimulus correlations. The within-AI dual-electrode peak correlation was higher than that across areas, especially for spontaneous conditions. Correlation strengths for between area pairs were independent of the difference in characteristic frequency (CF), thereby providing a mechanism of feature binding for broadband sounds. For noise-burst stimulation, the onset correlation for between area pairs was independent of stimulus intensity regardless the difference in CF. In contrast, for tone-pip stimulation a significant dependence on intensity level of the peak correlation strength was found for pairs involving AI and/or AAF with CF difference less than one octave. Across all areas, driven rate, between-area peak correlation strength, or a combination of the two did not predict stimulus intensity. However, between-area peak correlation strength performs better than firing rate to decide if a stimulus is present or absent.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".