Estimating the Excitatory-Inhibitory Balance from Electrocorticography Data using Physics-Informed Neural Networks
Bibliographic record
Abstract
Understanding the excitatory/inhibitory (E/I) balance in the brain is crucial for elucidating the neural mechanisms underlying various cognitive functions and states of consciousness. Mathematical models have provided significant insights into these mechanisms, but they often face challenges due to high dimensionality, noisy observation signals, and nonlinearities. In this paper, we introduce a novel methodology using Physics-Informed Neural Networks (PINNs) to estimate the E/I balance from electrocorticography (ECoG) data, effectively addressing these limitations. By integrating physical laws via a neural mass model with neural network training, our approach enhances parameter estimation accuracy and robustness. Our analysis reveals a significant reduction in long-range connections (LRCs) and excitatory short-range connections (SRCs) under anesthesia, alongside an increase in inhibitory SRCs, highlighting anesthesia’s role in modulating neural dynamics to induce unconsciousness. These findings not only corroborate existing theories on the neural mechanisms of anesthesia but also provide new insights into brain connectivity and its relationship with consciousness. CCS CONCEPTS Computing methodologies→Machine learning ACM Reference Format
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".