Deriving connectivity from spiking activity in detailed models of large-scale cortical microcircuits
Bibliographic record
Abstract
Abstract Inferring detailed cortical microcircuit connectivity is essential for uncovering how information is processed in the brain. A common method in vivo uses short-lag spike cross-correlations to derive putative monosynaptic connections, but inactive neurons and correlated firing can hinder the derivation accuracy. Previous computational studies that developed methods to derive connectivity from cross-correlations employed simplified or small network models and thus did not address the above key confounds of physiological large-scale networks. We tested connectivity derivation using simulated ground-truth spiking from detailed models of human cortical microcircuits in different layers and between key neuron types. While derivation accuracy was high for cortical layer 5 microcircuits, we showed that low-firing and inactive neurons in layer 2/3 microcircuits resulted in poor performance. We then showed that general activation paradigms for layer 2/3 microcircuits led to only a moderate improvement in derivation performance, due to a trade-off between reducing the proportion of inactive neurons and increasing correlated overactive neurons. We further improved the connection derivation performance using a more refined activation paradigm leading to jittered moderate spiking, which decreased inactive neurons without incurring unwanted correlations. Our results address key physiological challenges and provide methods to improve performance in deriving connections from spiking activity in large-scale neuronal microcircuits.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".