An Efficient Adaptive Online Neural Spikes Detection and Classification Engine Based on Bayesian Inference
Bibliographic record
Abstract
A new method, called Bayesian inference-based template matching (BIBTM) method, is proposed in this article, which is designed to detect and classify neural spikes from real neural signals. Through this spike detection and classification method, the templates do not need be given in advance, and they can be automatically generated. To evaluate the performance of our method, we built signals with diverse signal-to-noise ratios and firing rates, and also researched two spike template generation methods. Based on the experimental results and comparison, BIBTM method has excellent detection performance. The true positive rates (TPR) and false positive rates (FPR) of the spike detection can reach 0.92 and 0.05 respectively, and the average FPR and average TPR of the spike classification can reach 0.05 and 0.6 respectively. From the discussion and analysis, our proposed BIBTM method not only has high detection and classification accuracy, but also has a simple structure and low complexity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".