Emergent phenomena in human EEG: a bifurcation theory approach
Bibliographic record
Abstract
A recently proposed mesoscopic mean-field theory of mammalian cortex electro-rhythmogenesis describes the salient features of electrical activity in the cerebral macrocolumn, with the use of inhibitory and excitatory neuronal populations [1]. Numerical solutions to the defining non-linear equations have revealed two interesting emergent phenomena: i) the appearance of large-scale spatial coherent 40 Hz oscillations in the model cortex, which has shown to be experimentally related to cognitive activity [2], and ii) a surge in spectral EEG power – when the brain is subject to general anesthetics – as a function of their concentration [3], a clinically established effect known as the "biphasic" response. Given that quasi-linear approximations have been unable to capture the dynamical mechanism that trigger these phenomena, a bifurcation analysis [4] of physiologically relevant parameter sets was performed. Because of the predicted importance of inhibitory-inhibitory population strength in determining many fundamental dynamical features of the model, bifurcation parameters were chosen that modified inhibitory post-synaptic potential amplitudes and anatomical coupling strengths of the inhibitory neural population (respectively R and k in Figure 1). The analysis revealed the existence of a pattern of recurring bifurcation diagrams and specific "phase" transitions that constitute the dynamical requirements for the genesis and unfolding of 40 Hz and biphasic behaviors. This result also reaffirms the relevance of inhibition for the genesis of global collaborative events in the cortex and sheds light on the organization of the parameter space and the variability of EEG in the model respect to those perturbations and collective responses that are physiologically admissible.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".