Digital dementia and testing of cognitive intervention for degenerating neural networks
Bibliographic record
Abstract
The development of effective interventions for neurodegenerative disorders, such as posterior cortical atrophy (a visual Alzheimer's variant), remains to be a significant clinical challenge. We introduce a computational framework using convolutional neural networks (CNNs) as in silico models to simulate visual system degeneration and evaluate intervention strategies. By modeling controlled synaptic decay and comparing three distinct retraining approaches, random data (control), accuracy-based, and entropy-based, we assess impacts on classification performance and neural representation geometry. Our results demonstrate that accuracy-based retraining outperformed other strategies, maintaining model performance and preserving optimal manifold geometry during intermediate degeneration stages. This computational analysis supports prioritizing accuracy-targeted interventions for cognitive compensation. Our framework enables rapid evaluation of intervention efficacy while elucidating computational principles underlying neurodegeneration and recovery. This approach offers a platform for refining strategies to slow visual-cognitive decline in neurodegenerative diseases, bridging mechanistic insights with clinical translation.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".