An automatic measure for classifying clusters of suspected spikes into single cells versus multiunits
Bibliographic record
Abstract
While automatic spike sorting has been investigated for decades, little attention has been allotted to consistent evaluation criteria that will automatically determine whether a cluster of spikes represents the activity of a single cell or a multiunit. Consequently, the main tool for evaluation has remained visual inspection by a human. This paper quantifies the visual inspection process. The results are well-defined criteria for evaluation, which are mainly based on visual features of the spike waveform, and an automatic adaptive algorithm that learns the classification by a given human and can apply similar visual characteristics for classification of new data. To evaluate the suggested criteria, we recorded the activity of 1652 units (single cells and multiunits) from the cerebrum of 12 human patients undergoing evaluation for epilepsy surgery requiring implantation of chronic intracranial depth electrodes. The proposed method performed similar to human classifiers and obtained significantly higher accuracy than two existing methods (three variants of each). Evaluation on two synthetic datasets is also provided. The criteria are suggested as a standard for evaluation of the quality of separation that will allow comparison between different studies. The proposed algorithm is suitable for real-time operation and as such may allow brain-computer interfaces to treat single cells differently than multiunits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".