The Effect of Neural Entropy during Deep Brain Stimulation of Cortex-Basal Ganglia Network Model.
Bibliographic record
Abstract
Researchers reported decreased nerve entropy Patients with Parkinson's disease (PD) have abnormalities in their basal ganglia (BG). Studies of local field potentials (LFPs) recorded from the hypothalamus and single unit recordings of GP neurons showed this reduction to be significant. According to this hypothesis, these changes are consistent with changes in the ability of the basal ganglion network to encode PD information. Our deep brain stimulation of cortical basal ganglia (DBS) model includes single LFP recordings and shows how entropy changes during DBS. In addition to the extracellular stimulation of supplied STN fibers and LFP mimetics, which are detected differently on a registered electrode, this model includes osteoclast activation and anti-apoptosis. In the DBS network, the firing pattern fluctuated between high-frequency and low-frequency stimuli, since gp neurons in the network showed a decrease in entropy when a high-frequency stimulus was applied and an increase in entropy when a low-frequency stimulus was applied. Second hand. Changes in neural entropy after DBS have been reported experimentally. The simulation results were consistent
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".