Analysis of fractal electrodes for efficient neural stimulation
Bibliographic record
Abstract
Planar electrodes are increasingly used in a variety of neural stimulation techniques such as epidural spinal cord stimulation, epidural cortical stimulation, transcranial direct current stimulation and functional electric stimulation. Recently, optimized electrode geometries have been shown to increase the efficiency of neural stimulation by maximizing the variation of current density on the electrode surface. In the present work, a new family of modified fractal electrode geometries is developed to increase the neural activation function and enhance the efficiency of neural stimulation. It is hypothesized that the key factor in increasing the activation function in the tissue adjacent to the electrode is to increase the "edginess" of the electrode surface, a concept that is explained and quantified by fractal mathematics. Rigorous finite element simulations were performed to compute the distribution of electric potential produced by proposed geometries, demonstrating that the neural activation function was significantly enhanced in the tissue. The activation of 800 model axons positioned around the electrodes was also quantified, showing that modified fractal geometries yielded a 22% reduction in input power consumption while maintaining the same level of neural activation. The results demonstrate the feasibility of increasing stimulation efficiency using modified fractal geometries beyond the levels already reported in the literature.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".