Large-scale neuro-modeling for understanding and explaining some brain-related chaotic behavior
Bibliographic record
Abstract
Numerous studies, and in particular the works of Freeman and others, have shown the possible relevance of chaos to various functions of the brain. The verification of these claims has mostly been experimental in nature, since a formal mathematical analysis is often intractable because of the sheer magnitude of the number (billions) of neurons in the brain. Consequently, a formal analysis of such models has only been achieved for ‘small-scale systems’ of neural networks (NNs). As opposed to this, the aim of this paper is to understand how we can model the brain as a so-called ‘large-scale system’ for analyzing various neurological conditions such as epilepsy, schizophrenia, etc. In particular, we explore a large-scale NN model suitable for the piriform cortex which is acclaimed for its chaotic behavior from clinical experiments. In addition, the piriform cortex is easy to model because it appears to be ‘almost independent’ of other portions of the brain. To achieve this, we describe its behavior by moving the analysis from the time space into the phase space of the electroencephalogram (EEG) signals. Although the model of the piriform cortex contains hundreds of variables, we utilize the concept that useful information can be extracted from a single EEG signal which, in turn, can be perceived as a time series computed from the artificial electrodes. Indeed, this transformation, i.e. from the time space of a time series to its corresponding phase space, is considered mandatory to extract the nonlinear characteristics related with chaos. By studying the piriform cortex model in this manner, we demonstrate that we can generate certain desirable phenomena by modifying some of the underlying control parameters. More specifically, we investigate the problem of density and strength, the problem of connectivity and the problem of stimulus frequency, and show their relevance to neurological conditions such as epilepsy and schizophrenia.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".