Forebrain E-I balance controlled in cognition through coordinated inhibition and inhibitory transcriptome mechanism
Bibliographic record
Abstract
Introduction: Forebrain neural networks are vital for cognitive functioning, and their excitatory-inhibitory (E-I) balance is governed by neural homeostasis. However, the homeostatic control strategies and transcriptomic mechanisms that maintain forebrain E-I balance and optimal cognition remain unclear. Methods: We used patch-clamp and RNA sequencing to investigate the patterns of neural network homeostasis with suppressing forebrain excitatory neural activity and spatial training. Results: We found that inhibitory transmission and receptor transcription were reduced in tamoxifen-inducible Kir2.1 conditional knock-in mice. In contrast, spatial training increased inhibitory synaptic connections and the transcription of inhibitory receptors. Discussion: Our study provides significant evidence that inhibitory systems play a critical role in the homeostatic control of the E-I balance in the forebrain during cognitive training and E-I rebalance, and we have provided insights into multiple gene candidates for cognition-related homeostasis in the forebrain.
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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 | 0.001 | 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".