Bibliographic record
Abstract
Despite the wealth of studies on the dynamic characteristics of peripheral auditory neurons, very little has been reported on the higher statistical moments of the neural spike train. The notable exception is the study by Teich and Khanna (1985) where both the mean and the variance of the neural count are reported. The simplest model one can ascribe to a neural spike train is a homogeneous Poisson process. However, experimental data do not bear out such predictions. Other models have been proposed but the general consensus is that the underlying process is far from simple. We offer an alternative account of the fluctuations that occur at the peripheral level. Our explanation does not rely on assumptions regarding the process underlying individual spikes. Instead, we make use of the information-theoretical model of the neuron that we have been developing over the past 10 years (Norwich and Wong, 1995; Wong, 1997). The two key results predicted by the model are that (a) the mean-variance ratio has an approximate value of 2 and (b) the distribution governing the neural count is Gaussian to a good approximation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".