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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".