Neural field modeling and analysis of consciousness states in the brain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Understanding the neural correlates of consciousness remains a central challenge in neuroscience. In this study, we explore the potential of neural field theory (NFT) as a computational framework for representing consciousness states. While prior research has validated NFT’s capacity to differentiate between normal and pathological states of consciousness, the relationship of its parameters to the representation of consciousness states remains unclear. Here, we fitted a corticothalamic NFT model to the electroencephalography (EEG) data collected from healthy individuals and patients with disorders of consciousness. We then comprehensively explored the correlations between the fitted NFT parameters and features extracted from both experimental and simulated EEG data across various states of consciousness. The identified correlations not only highlight the model’s ability to differentiate between healthy and impaired states of consciousness, but also shed light on the physiological bases of these states, pinpointing potential biomarkers. Our results provide valuable insights into how consciousness levels are represented within the NFT framework and into the dynamics of brain activity across normal and pathological states of consciousness. This underscores the potential of NFT as a useful tool for consciousness research, facilitating in-silico experimentation.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it