NMDA Receptor Enhances Correlation of Spontaneous Activity in Neonatal Barrel Cortex
Bibliographic record
Abstract
Correlated spontaneous activity plays critical role in the organization of neocortical circuits during development. However, cortical mechanisms regulating activity correlation are still elusive. In this study, using two-photon calcium imaging of the barrel cortex layer 4 (L4) in living neonatal mice, we found that NMDA receptors (NMDARs) in L4 neurons are important for enhancement of spontaneous activity correlation. Disruption of GluN1 ( Grin1 ), an obligatory NMDAR subunit, in a sparse population of L4 neurons reduced activity correlation between GluN1 knock-out (GluN1KO) neuron pairs within a barrel. This reduction in activity correlation was even detected in L4 neuron pairs in neighboring barrels and most evident when either or both of neurons are located on the barrel edge. Our results provide evidence for the involvement of L4 neuron NMDARs in spatial organization of the spontaneous firing activity of L4 neurons in the neonatal barrel cortex. SIGNIFICANCE STATEMENT Precise wiring of the thalamocortical circuits is necessary for proper sensory information processing, and thalamus-derived correlated spontaneous activity is important for thalamocortical circuit formation. The molecular mechanisms involved in the correlated activity transfer from the thalamus to the neocortex are largely unknown. In vivo two-photon calcium imaging of the neonatal barrel cortex revealed that correlated spontaneous activity between layer four neurons is reduced by mosaic knock-out (KO) of the NMDA receptor (NMDAR) obligatory subunit GluN1. Our results suggest that the function of NMDARs in layer four neurons is necessary for the communication between presynaptic and postsynaptic partners during thalamocortical circuit formation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".