Principal dynamic mode analysis of a spider mechanoreceptor action potentials
Bibliographic record
Abstract
The nonlinear dynamics of a spider mechanoreceptor are examined using the principal dynamic mode (PDM) methodology. The cuticilar sense organ of an adult Cupiennius Salei spider was stimulated with a pseudorandom Gaussian process spectrally bound at 400 Hz and the resulting action potentials were recorded. Data analysis reveals three PDMs that describe the system dynamics, based on the second order Volterra kernel, which is estimated from the recorded input/output datasets. The first PDM exhibits high-pass behavior, illustrating the importance of the speed of the slit displacement to the mechanoreceptor, while the other two PDMs exhibit bandpass behavior. The probability of firing an action potential is represented by a static multiple-input nonlinearity that receives the values of the convolution of each mode with the pseudorandom input as its inputs. The probability of firing function exhibits asymmetric behavior with respect to its arguments, suggesting directional dependence of the mechanoreceptor response on the PDM outputs. Trigger regions for a probability threshold value of 0.1 are also presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".