Comparison of two populations of curves with an application in neuronal data analysis
Bibliographic record
Abstract
Often in neurophysiological studies, scientists are interested in testing hypotheses regarding the equality of the overall intensity functions of a group of neurons when recorded under two different experimental conditions. In this paper, we consider such a hypothesis testing problem. We propose two test statistics: a parametric test similar to the modified Hotelling's T2 statistic of Behseta and Kass (Statist. Med. 2005; 24:3523–3534), as well as a nonparametric one similar to the spatial signed-rank test statistic of Möttönen and Oja (J. Nonparametric Statist. 1995; 5:201–213). We implement these tests on smooth curves obtained via fitting Bayesian Adaptive Regression Splines (BARS) to the intensity functions of neuronal Peri-Stimulus Time Histograms. Through simulation, we show that the powers of our proposed tests are extremely high even when the number of sampled neurons and the number of trials per neuron are small. Finally, we apply our methods on a group of motor cortex neurons recorded during a reaching task.
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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 | 0.020 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".