Dynamics of thalamocortical circuits for sound processing revealed by magnetoencephalography.
Bibliographic record
Abstract
Music perception and cognition involves multi-modal processing within a wide range of neural networks working in concert. Rhythmic brain activities, or neural oscillations, are thought to play an important role in such long-range communication. How are related networks established and dynamically reconfigured in order to adapt to the ever changing auditory environment? Oscillations in the 40-Hz range (gamma band) in thalamocortical connections are proposed as a key mechanism. A common problem to delineate the behavior of 40-Hz oscillatory activity, however, is the small effect size and unknown time, courses when using noninvasive megnetoencephalography (MEG) recording. To overcome this problem, auditory stimulation with sounds containing a strong 40-Hz rhythm can be used to drive neural networks into a state of high synchrony. These areas are successfully identified by beamforming techniques, which transform MEG signals to voxel-based source images. Phase lags between primary auditory cortices and thalamus and auditory association areas suggest the information flow across the regions. Moreover, changes in the sound stimulus were observed as temporal changes of synchrony reflecting dynamic reconfiguration of neural networks. The relevance of these observations for detecting changes in sound localization will be demonstrated.
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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) | 0.001 | 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".