Human first-order tactile neurons can resolve spatial details on the scale of single fingerprint ridges
Bibliographic record
Abstract
Abstract Fast-adapting type 1 (FA-1) and slow-adapting type 1 (SA-1) first-order tactile neurons provide detailed spatiotemporal tactile information when we touch objects with fingertips. The distal axon of these neuron types branches in the skin and innervates many receptor organs associated with fingerprint ridges (Meissner corpuscles and Merkel cell neurite complexes, respectively), resulting in heterogeneous receptive fields that include many highly sensitive zones or ‘subfields’. Using raised dots that tangentially scanned a neuron’s receptive field, here we examined the spatial resolution capacity of FA-1 and SA-1 neurons afforded by their heterogeneous receptive fields and its constancy across scanning speed and direction. We report that the resolution of both neuron types on average corresponds to a spatial period of ∼0.4 mm and provide evidence that a subfield’s spatial selectivity arises because its associated receptor organ measures mechanical events limited to a single fingerprint ridge. Accordingly, the sensitivity topography of a neuron’s receptive fields is quite stable over repeated mappings and over scanning speeds representative of real-world hand use. The sensitivity topography is substantially conserved also for different scanning directions, but the subfields can be relatively displaced by direction-dependent shear deformations of the skin surface. Significance Statement The branching of the distal axon of first-order tactile neurons with receptor-organs associated with fingerprint ridges (Meissner and Merkel end-organs) results in cutaneous receptive fields composed of several distinct subfields spread across multiple ridges. We show that the spatial selectivity of the subfields typically corresponds to the dimension of the ridges (∼0.4 mm) and that neurons’ subfield layout is well preserved across tangential movement speeds and directions representative of natural use of the fingertips. We submit that the receptor-organ underlying a subfield essentially measures mechanical events at an individual ridge. That neurons receive convergent input from multiple subfields does not preclude the possibility that spatial details can be resolved on the scale of single fingerprint ridges by a population code.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".