Modeling foot sole cutaneous afferents: FootSim
Bibliographic record
Abstract
While walking and maintaining balance, humans rely on cutaneous feedback from the foot sole. Electrophysiological recordings reveal how this tactile feedback is represented in neural afferent populations, but obtaining them is difficult and limited to stationary conditions. We developed the FootSim model, a realistic replication of mechanoreceptor activation in the lower limb. The model simulates neural spiking responses to arbitrary mechanical stimuli from the combined population of all four types of mechanoreceptors innervating the foot sole. It considers specific mechanics of the foot sole skin tissue, and model internal parameters are fitted using human microneurography recording dataset. FootSim can be exploited for neuroscientific insights, to understand the overall afferent activation in dynamic conditions, and for overcoming the limitation of currently available recording techniques. Furthermore, neuroengineers can use the model as a robust in silico tool for neuroprosthetic applications and for designing biomimetic stimulation patterns starting from the simulated afferent neural responses.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".