Synchronization Control of Interconnected Systems with Applications to Neuronal Models
Why this work is in the frame
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Bibliographic record
Abstract
This thesis investigates the control of synchronization for a general class of compartmental models. The system consists of interconnected compartments. Each compartment is an interconnection of subsystems called species, which are modeled as input-output operators in the extended L_2 space. Compartmental models are ubiquitous in biological networks modeling. The distinctive feature of this thesis is the presence of an external feedback loop that can be used to modify the behavior of the network. Our main results provide controllable bounds on the level of synchrony of the network. The problem ofselecting the feedback parameters to achieve the desired bounds is also addressed and the approach is applied to a number of neuronal network models. The application is motivated by the feedback control of pathological synchronization of neuron firing in the brain, often responsible for neurological diseases such as Parkinsons
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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