Morphological variability may limit single-cell specificity to electric field stimulation
Bibliographic record
Abstract
Introduction: Non-invasive brain stimulation techniques, widely used to manipulate neural excitability and behavior, are well studied at the meso- and macroscopic scales. However, less is known about their specificity at the level of individual cells. Methods: Models based on real pyramidal and parvalbumin neuron morphologies created by the Allen Institute for Brain Science were characterized using metrics we devised to quantify various aspects of cellular morphology, ranging from whole cell attributes to net compartment length, branching, diameter and orientation. The models were simulated to quantify the single-cell variability and evoked response susceptibility to uniform electric fields. Results and discussion: No physical traits yielded layer- or cell-type-specific responses passing statistical significance tests. While uniform electric fields reliably modulated somatic, dendritic and axonal compartments, and subtype-specific responses were observed, specificity was blurred by the variability in cellular morphology. These null results suggest morphology alone may not account for the reported subtype specificity to electric field stimulation, and question the extent to which non-invasive techniques can control specific components of neural circuitry.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".