Postsynaptic Dorsal Column and Cuneate Correlations in the Raccoon: A Re-evaluation by Parallel-Cascade Analysis
Bibliographic record
Abstract
In a previous study, we reported evidence for correlations between the firing of postsynaptic dorsal column (PSDC) neurons and cuneate neurons with overlapping receptive fields on the glabrous skin of the raccoon forepaw. The evidence was based on cross-correlation and frequency response analyses of spontaneously firing neurons. However, cross-correlation without white noise Gaussian analog inputs or Poisson distributed pulse train inputs is difficult to interpret because of the inherent convolution with the autocorrelation of the unknown input signals. While the data suggested positive correlations in the spinocuneate direction for most neuron pairs, we could not estimate the temporal characteristics of these putative connections. We have now re-analyzed these data using a parallel-cascade method to estimate the first- and second-order kernels of a Volterra series approximation to the spinocuneate system. This unbiased analysis suggests that a positive correlation occurs after about 5 ms, probably followed by a negative correlation at about 12 ms. Second-order kernels also had repeatable structure, indicating dual pathways with time separations of at least 10 ms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".