Functional Refinement in the Projection from Ventral Cochlear Nucleus to Lateral Superior Olive Precedes Hearing Onset in Rat
Bibliographic record
Abstract
Principal neurons of the lateral superior olive (LSO) compute the interaural intensity differences necessary for localizing high-frequency sounds. To perform this computation, the LSO requires precisely tuned, converging excitatory and inhibitory inputs that are driven by the two ears and that are matched for stimulus frequency. In rodents, the inhibitory inputs, which arise from the medial nucleus of the trapezoid body (MNTB), undergo extensive functional refinement during the first postnatal week. Similar functional refinement of the ascending excitatory pathway, which arises in the anteroventral cochlear nucleus (AVCN), has been assumed but has not been well studied. Using whole-cell voltage clamp in acute brainstem slices of neonatal rats, we examined developmental changes in input strength and pre- and post-synaptic properties of the VCN-LSO pathway. A key question was whether functional refinement in one of the two major input pathways might precede and then guide refinement in the opposite pathway. We find that elimination and strengthening of VCN inputs to the LSO occurs over a similar period to that seen for the ascending inhibitory (MNTB-LSO) pathway. During this period, the fractional contribution provided by NMDA receptors (NMDARs) declines while the contribution from AMPA receptors (AMPARs) increases. In the NMDAR-mediated response, GluN2B-containing NMDARs predominate in the first postnatal week and decline sharply thereafter. Finally, the progressive decrease in paired-pulse depression between birth and hearing onset allows these synapses to follow progressively higher frequencies. Our data are consistent with a model in which the excitatory and inhibitory projections to LSO are functionally refined in parallel during the first postnatal week, and they further suggest that GluN2B-containing NMDARs may mediate early refinement in the VCN-LSO pathway.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".