The role of synchronous spiking in the encoding of vibrotactile stimuli by low-threshold mechanoreceptors
Bibliographic record
Abstract
Despite its known importance in most other sensory systems, the role of synchrony remains to be explored in somatosensation. Spike synchrony is vital for signal propagation between low-threshold mechanoreceptors (LTMRs) and their postsynaptic targets, but the influence of different tactile stimuli on LTMR spike synchrony remains unclear. In response to a periodic stimulus like vibration, synchronous spiking across neurons relies on the timing of spikes relative to the phase of the stimulus cycle (i.e. precision) and the probability of a spike occurring on each cycle (i.e. reliability). As such, through in vivo extracellular recordings in rodents, we measured the reliability and precision of rapid adapting (RA)- and slow adapting (SA)-LTMR responses to vibrotactile stimuli to infer synchronization of spiking across neurons. Results showed that SA and RA afferents synchronize at different frequency ranges. Interestingly, population synchrony was lost at low and high frequencies due to a loss of spiking precision or reliability, respectively. To explore the mechanisms supporting synchrony loss at each frequency extreme, we developed generalized linear models of LTMRs. Differences in the fitted model parameters demonstrate that a shorter refractory period gives RAs the unique ability to respond to and synchronize at high frequencies. The findings of this study strengthen our understanding of synchrony in somatosensory coding and the resulting models allow for efficient exploration of the mechanisms underlying tactile signal processing.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".