Electro-Optic Fiber Photometry Front-End for Live Wireless Brain Calcium Sensing Applications
Bibliographic record
Abstract
The study of the brain is crucial to its understanding and the pursuit of medical research. Whether it be at the scale of the brain itself or at the scale of a single neuron, there exists a multitude of techniques used to visualize its activity in order to study its inner working mechanisms. To study a specific group of neurons, the preferred method is fiber photometry, which allows to monitor a specific group of neurons marked by a chosen calcium indicator (CI). Calcium fluorescence sensing through fiber photometry is an imagery technique often used with live rodents (usually mice) to visualize neuronal activity while performing a specific task or natural behavior. This is typically done by implanting a probing optical fiber into the brain region under study. The drawback of conventional fiber photometry systems is that the optical fiber connected to the subject’s head limits its movements and creates stress on the mouse. This work proposes an electro-optic front-end for a wireless fiber photometry system with performances worthy of current existing cabled commercial systems. The design of this system rests on the conception of high-performance analog circuits and the development of signal processing algorithms. The system designed in this work offers a reading with an isosbestic point reference with a noise equivalent power (NEP) of 5.4 fW/$\sqrt {\text {Hz}}$. The prototype built using inexpensive off the shelf electronic components has a low power consumption of 25.6 mW, where only 5.1 mW is used by the electronic components and the rest is consumed by the light emitting diodes (LEDs) to generate an excitation signal. Thanks to the use of a digital lock-in amplifier, the prototype has a small size and weight while enjoying a 30 dB increase in signal to noise ratio (SNR) in the photometry reading. The prototype was tested in live mice during in vivo neuronal photometry recording conducted in the lateral hypothalamus area (LHA) in response to an aversive stimulus.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".