A Biologically-Informed Computational Framework for Pathway-Specific Spiking Patterns Generation and Efficacy Evaluation in Retinal Neurostimulators
Bibliographic record
Abstract
Precise control of neuronal pathways is of critical importance for the efficacy of neuro-modulation prostheses designed to restore vision in patients with retinal degeneration. In this paper, a biologically-informed computational framework that enables generation and optimization of pathway-specific spiking patterns for retina ganglion cells (RGCs) is presented. The developed model is used to generate control signals for an implantable optical retinal neurostimulator device to modulate genetically-modified RGCs, thus enable cell-type-specific neural excitation. The model is also used to compare electrical and optical neuro-stimulations with respect to the quality of the visual perception they induce in the brain for the same visual stimulus. Our simulation results show that the type-specific activation of RGCs (an inherent advantage of optogenetics) makes optical stimulation significantly more effective than electrical stimulation in achieving high structural similarity between the visual stimulus and the estimated perception. The effect of increasing /-Lelectrode//-LLED array density to achieve a better performance in terms of the induced visual perception quality by optical and electrical stimulations was also studied using the developed model. The superiority of optical stimulation in this aspect was demonstrated through simulation results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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) | 0.000 | 0.000 |
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 teacher head, 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".