slap2-utils: Tools for Processing SLAP2 Data
Bibliographic record
Abstract
Two-photon microscopy enables the measurements of neural activity in vivo, deep within the brain using fluorescent biosensors.However, capturing neural activity with voltage indicators, fast calcium or neurotransmitter biosensors, or slower indicators across 3D volumes requires sampling speeds that far exceed capabilities of conventional laser-scanning two-photon microscopes.The recently developed Scanned Line Angular Projection-2 (SLAP2) microscope has overcome these limitations by employing novel ultra-fast random-access scanning techniques to record neural activity at kilohertz sampling rates.Unlike traditional raster imaging, where pixels are recorded sequentially by spatial position, SLAP2's random-access acquisition optimizes the sampling order of regions of interest for speed.These recordings are stored in a complex file structure, which tracks the non-sequentially sampled coordinates.This novel data structure is incompatible with post-imaging analysis tools designed for traditional image formats, such as TIF or CZI files.Here, we present our Python library, slap2-utils, to interact with the custom data structure generated from SLAP2.slap2-utils allows users to extract neuronal activity directly from the SLAP2 binary files in a Python environment.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.057 | 0.037 |
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".