Light distribution properties in spinal cord for optogenetic stimulation(Conference Presentation)
Bibliographic record
Abstract
Optogenetics is currently one of the most popular technique in neuroscience. It enables cell-selective and temporally-precise control of neuronal activity. Good spatial control of the stimulated area and minimized tissue damage requires a specific knowledge about light scattering properties. Light propagation in cell cultures and brain tissue is relatively well documented and allows for a precise and reliable delivery of light to the neurons. In spinal cord, light must pass through highly organized white matter before reaching cell bodies present in grey matter, this heterogenous structure makes it difficult to predict the propagation pattern. In this work we investigate the light distribution properties through mouse and monkey spinal cord. The light propagation depends on a fibers orientation, leading to less deep penetration profile in the direction perpendicular to the fibers and lower attenuation in the direction parallel to the fibers. Additionally, the use of different illumination wavelengths results in variations of the attenuation coefficient. Next, we use Monte-Carlo simulation to study light transport. The model gives a full 3-D simulation of light distribution in spinal cord and takes into account different scattering properties related to the fibers orientation. These studies are important to estimate the minimum optical irradiance required at the fiber tip to effectively excite the optogenetic proteins in a desired region of spinal cord.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".