eLife assessment: Multi-day neuron tracking in high-density electrophysiology recordings using earth mover’s distance
Bibliographic record
Abstract
Accurate tracking of the same neurons across multiple days is crucial for studying changes in neuronal activity during learning and adaptation.Advances in high-density extracellular electrophysiology recording probes, such as Neuropixels, provide a promising avenue to accomplish this goal.Identifying the same neurons in multiple recordings is, however, complicated by non-rigid movement of the tissue relative to the recording sites (drift) and loss of signal from some neurons.Here, we propose a neuron tracking method that can identify the same cells independent of firing statistics, that are used by most existing methods.Our method is based on between-day nonrigid alignment of spike-sorted clusters.We verified the same cell identity in mice using measured visual receptive fields.This method succeeds on datasets separated from 1 to 47 days, with an 84% average recovery rate. eLife assessmentThis important study proposes a new method for tracking neurons recorded with Neuropixel electrodes across days.The methods and the strength of the evidence are convincing, but the authors do not address whether their approach can be generalized to other brain areas, species, behaviors, or tools.Overall, this method will be potentially of interest to many neuroscientists who want to study long-term activity changes of individual neurons in the brain.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.012 |
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".