Postsynaptic and Presynaptic NMDARs Have Distinct Roles in Visual Circuit Development
Bibliographic record
Abstract
To study contributions of N-methyl-D-aspartate receptors (NMDARs) in presynaptic and postsynaptic neurons of the developing visual system, we microinject antisense Morpholino oligonucleotide (MO) against GluN1 into one cell of two-cell-stage Xenopus laevis embryos. The resulting bilateral segregation of MO induces postsynaptic NMDAR (postNMDAR) knockdown in tectal neurons on one side and presynaptic NMDAR (preNMDAR) knockdown in ganglion cells projecting to the other side. PostNMDAR knockdown reduces evoked NMDAR- and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR)-mediated retinotectal currents. Although the frequency of spontaneous synaptic events is increased, the probability of evoked release is reduced. PreNMDAR knockdown results in larger evoked and unitary synaptic responses. Structurally, postNMDAR and preNMDAR knockdown produce complementary effects. Axonal arbor complexity is reduced by preNMDAR-MO and increased by postNMDAR-MO, whereas tectal dendritic arbors exhibit the inverse. The current study illustrates distinct roles for pre- and postNMDARs in circuit development and reveals extensive transsynaptic regulation of form and function.
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 | 0.000 | 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".