Additional file 3 of mTORC1-mediated acquisition of reward-related representations by hippocampal somatostatin interneurons
Bibliographic record
Abstract
Additional file 3: Figure S3. SOM-INs with no response modulation; deceleration correlation of response types; activity correlation between cells; and response type determined across trials. A Three representative examples of SOM-IN responses with no modulation related to reward. Top: mean Ca2+ responses and speed as function of position for all trials in a session with reward zone indicated in green. Bottom: color-coded Ca2+ activity in each trial of the session. B Similar representation as in Fig. 3F of Ca2+ activity correlation with deceleration for SOM-INs with different response types, showing response-specific changes over training. C Mean activity correlation between all SOM-INs, showing less correlation in control mice relative to SOM-Rptor-KO mice. D Cell response identity matrix obtained with a subtype identification method based on significant difference of ΔF/F across trials at reward zone. Response identity for all cells over training sessions ordered by response type at end of training, showing a gradual acquisition of spatial coding related to reward location. Top of matrix: SOM-INs from control mice. Bottom: SOM-INs from SOM-Rptor-KO mice. E Distribution of cells with different response types at start and end of training for control and SOM-Rptor-KO mice using subtype identification method based on trials, showing presence of 4 response types in both mouse genotypes, but increases with training in number of cells with responses only in control mice. F Summary plot for all cells comparing response subtype changes across all training sessions using response classification methods based on analysis of mean or trial activity, showing greater response variability with trial analysis method in control mice. Details of statistical tests provided in Additional file 5: Table S1. * p < 0.05, ** p < 0.01, *** p < 0.001, ns not significant.
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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 | 0.001 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.895 | 0.234 |
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".