A Model for Single Neuron Activity With Refractory Effects and Spike Rate Estimation Techniques
Bibliographic record
Abstract
The use of random point processes as models for neural spike trains allows the derivation of powerful statistical estimation techniques for time varying firing rates. Frequently, however, such estimators are based on the assumption that spike sequences follow a Poisson point process. Because of the bio-physical properties of neuronal action potentials, spike trains are affected by the refractory phenomenon that induces history dependency, and hence contradicts the Poisson assumption. In this work we present a neural spiking model, and a Maximum Likelihood (ML) estimation framework for time varying firing rates, that account for history dependencies in spike trains. Our framework is based on an exponential of polynomial model for the excitation function (stimulus), that generates a self exciting point process representing spike trains with absolute as well as relative refractory effects. Using this framework we employ techniques based on non-convex optimization and model order selection to derive ML estimators for neuronal firing rates. Results on simulated data with a refractory period show an improvement in accuracy when our estimation technique, that accounts for the complete refractory phenomenon, is used. Employing this estimation method for measured neuronal data shows an improvement in goodness of fit over estimators that do not account for the refractory effect, and also over other commonly used techniques.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".