UnitRefine: A Community Toolbox for Automated Spike Sorting Curation
Bibliographic record
Abstract
Abstract High-density electrophysiology simultaneously captures the activity from hundreds of neurons, but isolating single-unit activity still relies on slow and subjective manual curation. As datasets keep increasing, this poses a major bottleneck in the field. We therefore developed UnitRefine, a classification toolbox that automates curation by training various machine-learning models directly on human expert annotations. Fully integrated in the SpikeInterface ecosystem, UnitRefine combines established and novel quality metrics, cascading classification and comprehensive hyperparameter search to provide optimized models for different applications. UnitRefine achieves human-level performance across diverse datasets, spanning species, probe types, and laboratories, including recordings from mice, rats, mole rats, primates, and human patients. Applied to a large brain-wide dataset, UnitRefine doubled single unit yield and improved behavioral decoding performance. A streamlined graphical interface allows models to be fine-tuned to new datasets and shared via the Hugging Face Hub, enabling broad adoption and community-driven improvement of automated curation workflows.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 0.040 |
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".