Novel method to assess axonal excitability using channelrhodopsin-based photoactivation
Bibliographic record
Abstract
Measuring the excitability of individual axons is complicated by the prohibitive difficulty in obtaining intracellular recordings. Here, we present an innovative methodology that enables local excitability to be measured anywhere in a channelrhodopsin (ChR2)-expressing neuron. The approach hinges on activating ChR2 in a spatially and temporally precise manner while recording the resulting spike train from a remote site. We validated this approach in primary afferent neurons (PANs). Initial encoding of somatosensory stimuli relies on transduction of the physical stimulus into a receptor potential and transformation of the receptor potential into a spike train; the transformation process depends on the excitability of the most distal PAN endings but, as explained above, is extraordinarily difficult to study in situ using traditional methods. Using ChR2-based photoactivation, we show 1) that excitability differs between the distal endings and more proximal portions of PAN axons, 2) that the transformation process differs between PANs, and 3) that the transformation process is directly affected by inflammation. Beyond presenting an innovative method by which to study axonal excitability, this study has validated its utility in helping to decipher the earliest stages of somatosensory encoding.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".