Synaptic integration across first-order tactile neurons can discriminate edge orientations with high acuity and speed
Bibliographic record
Abstract
Our ability to manipulate objects relies on tactile inputs signaled by first-order tactile neurons that innervate the glabrous skin of the hand. A fundamental feature of these first-order tactile neurons is that their distal axon branches in the skin and innervates many mechanoreceptive end organs, yielding spatially-complex receptive fields with several highly sensitive zones. Recent work indicates that this peripheral arrangement constitutes a neural mechanism enabling first-order tactile neurons to signal high-level geometric features of touched objects, such as the orientation of an edge moving across the skin. Here we show how second-order tactile neurons could integrate these complex peripheral signals to compute edge-orientation. We first derive spiking models of human first-order tactile neurons that fit and predict responses to moving edges with high accuracy. Importantly, our models suggest that first-order tactile neurons innervate mechanoreceptors in the fingertips following a random sampling scheme. We then use the model neurons as the basis for a simulation of the peripheral neuronal population and its synaptic integration by second-order tactile neurons (i.e. in the spinal cord and brainstem). Our model networks indicate that computations done by second-order neurons could underlie the human ability to process edge orientations with high acuity and speed. In particular, our models suggest that synaptic integration of AMPA inputs within short timescales are critical for discriminating fine orientations, whereas NMDA-like synapses refine discrimination and maintain robustness over longer timescales. Taken together, our results provide new insight into the computations occurring in the earliest stages of the tactile processing pathway and how they may be critical for supporting hand function.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".