Tone Frequency Maps and Receptive Fields in the Developing Chinchilla Auditory Cortex
Bibliographic record
Abstract
Single-unit responses to tone pip stimuli were isolated from numerous microelectrode penetrations of auditory cortex (under ketamine anesthesia) in the developing chinchilla (laniger), a precocious mammal. Results are reported at postnatal day 3 (P3), P15, and P30, and from adult animals. Hearing sensitivity and spike firing rates were mature in the youngest group. The topographic representation of sound frequency (tonotopic map) in primary and secondary auditory cortex was also well ordered and sharply tuned by P3. The spectral-temporal complexity of cortical receptive fields, on the other hand, increased progressively (past P30) to adulthood. The (purported) refinement of initially diffuse tonotopic projections to cortex thus seems to occur in utero in the chinchilla, where external (and maternal) sounds are considerably attenuated and might not contribute to the mechanism(s) involved. This compares well with recent studies of vision, suggesting that the refinement of the retinotopic map does not require external light, but rather waves of (correlated) spontaneous activity on the retina. In contrast, it is most probable that selectivity for more complex sound features, such as frequency stacks and glides, develops under the influence of the postnatal acoustic environment and that inadequate sound stimulation in early development (e.g., due to chronic middle ear disease) impairs the formation of the requisite intracortical (and/or subcortical) circuitry.
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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 | 0.001 | 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".