Experience-Dependent Changes in Excitatory and Inhibitory Receptor Subunit Expression in Visual Cortex
Bibliographic record
Abstract
Experience-dependent development of visual cortex depends on the balance between excitatory and inhibitory activity. This activity is regulated by key excitatory (NMDA, AMPA) and inhibitory (GABA(A)) receptors. The composition of these receptors changes developmentally, affecting the excitatory-inhibitory (E/I) balance and synaptic plasticity. Until now, it has been unclear how abnormal visual experience affects this balance. To examine this question, we measured developmental changes in excitatory and inhibitory receptor subunits in visual cortex following normal visual experience and monocular deprivation. We used Western blot analysis to quantify expression of excitatory (NR1, NR2A, NR2B, GluR2) and inhibitory (GABA(A)α1, GABA(A)α3) receptor subunits. Monocular deprivation promoted a complex pattern of changes in receptor subunit expression that varied with age and was most severe in the region of visual cortex representing the central visual field. To characterize the multidimensional pattern of experience-dependent change in these synaptic mechanisms, we applied a neuroinformatics approach using principal component analysis. We found that monocular deprivation (i) causes a large portion of the normal developmental trajectory to be bypassed, (ii) shifts the E/I balance in favor of more inhibition, and (iii) accelerates the maturation of receptor subunits. Taken together, these results show that monocularly deprived animals have an abnormal balance of the synaptic machinery needed for functional maturation of cortical circuits and for developmental plasticity. This raises the possibility that interventions intended to treat amblyopia may need to address multiple synaptic mechanisms to produce optimal recovery.
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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 | 0.000 |
| 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".