Natural variation in skin thickness argues for mechanical stimulus control by force instead of displacement
Bibliographic record
Abstract
The neural response to touch stimuli is influenced by skin properties as well as the delivery of stimuli. Here, we compare stimuli controlled by displacement and force, and analyze the impact on firing rates of slowly adapting type I afferents as skin thickness and elasticity change. Uniaxial compression tests were used to measure the mechanical properties of mouse hind limb skin (n=5), resulting in a range of skin thickness measurements (211.6-530.6 μm) and hyper- and visco-elastic properties (average coefficient of variation=0.27).Values were integrated to an axisymmetric finite element model using an Ogden strain energy function. This calculated the propagation of surface loads to tactile end-organ locations, where maximum compressive stress and its rate were sampled and linearly regressed to firing rate. For the observed range of skin thickness, firing response was predicted under both force and displacement control of a ramp-and-hold stimulus. Over the ramp phase of stimulation, the variance in predicted firing rate was higher under displacement than under force control (22.2versus 4.9 Hz) with a similar trend in the sustained phase of stimulation (4.6versus1.3Hz). Given that skin thickness varies significantly between specimens, for human skin perhaps seven more so than for mice, the use of force control is predicted to decrease experimental variance in neurophysiological and psychophysical 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 | 0.001 | 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 | 0.000 | 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".