Silicon Photomultiplier-based Low-light in vivo Fiber Photometry
Bibliographic record
Abstract
Fiber photometry is an important tool for studying neural activity in freely moving animals. Existing systems utilize photodetectors requiring a high bias voltage or high optical power. The former results in expensive bulky systems and the latter leads to photobleaching or phototoxicity. We present a low-light fiber photometry system for recording neural activity in mice. Employing a sensitive silicon photomultiplier (SiPM) allows the use of low excitation light without requiring high-voltage power supplies. Isosbestic wavelength control was implemented to correct for motion artifacts. The same control signal was used as a novel method for SiPM gain correction, eliminating the need for additional sensors and control mechanisms. An off-the-shelf impedance measurement integrated circuit was used to simplify the electronics for homodyne detection of light. Sensitivity, dynamic range, and robustness to artifacts were characterized by measurement of fluorescence in fluorescein solutions. In vivo measurements during footshock experiments validated the system’s effectiveness at 2.3 μW excitation power. The system’s power requirements show promise for miniaturization and animal-mountable configurations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".