Emergent complex patterns in autonomous distributed systems: mechanisms for attention recovery and relation to models of clinical epilepsy
Bibliographic record
Abstract
Dynamical systems based on distributed elements can exhibit complex autonomous behavior. Simultaneous existence of separate stable dynamic states (attractors) and the transitions between them can model certain forms of epileptic discharge. Multi-stable systems have also been proposed for storage and retrieval of activation patterns. Here we consider systems with alternative types of collective behavior. In these systems emergent intermittency allows for autonomous switching between turbulent (chaotic) and laminar phases. We demonstrate that the distributions of the duration of various phases have distinctive statistical properties, different from those in multi-stable systems that are driven by stochastic processes. These properties are proposed to identify and classify mechanisms that may underlie paroxysmal activity as revealed in electrophysiological recordings of epileptiform activity. Unlike spontaneous stochastically-driven ictal transitions in multi-stable systems, certain features of intermittency-based transitions can, in principle, be forecasted and perhaps even ameliorated. We show that intermittency in a recurrent network does not require plastic connections. At the same time, we argue that an autonomous system with modifiable connections might require intermittent transition mechanisms in order to sustain proper connectivity and function. Networks showing intermittency avoid lockups and at the same time respond robustly and commensurably to dynamical input perturbation. They may thus provide a candidate mechanism for pattern recognition and attention recovery in biological and artificial systems.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".