Ultrafast traveling wave dominates the electric organ discharge of Apteronotus leptorhynchus: an inverse modelling study
Bibliographic record
Abstract
Identifying and understanding the current sources that give rise to bioelectric fields is a fundamental problem in the biological sciences. It is very difficult, for example, to attribute the time-varying features of an electroencephalogram recorded from the head surface to the neural activity of specific brain areas; model systems can provide important insight into such problems. Some species of fish actively generate an oscillating (c. 1000 Hz) quasi-dipole electric field to communicate and sense their environment in the dark. A specialized electric organ comprises neuron-like cells whose collective signal underlies this electric field. As a step towards understanding the detailed biophysics of signal generation in these fish, we use an anatomically-detailed finite-element modelling approach to reverse-engineer the electric organ signal over one oscillation cycle. We find that the spatiotemporal profile of current along the electric organ constitutes a travelling wave that is well-described by two spatial Fourier components varying in time. The conduction velocity of this wave is faster than action potential conduction in any known neuronal axon (>200 m/s), suggesting that the spatiotemporal features of high-frequency electric organ discharges are not constrained by the conduction velocities of spinal neuron pathways.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".