Towards realistic simulation of disease progression in the visual cortex with CNNs
Bibliographic record
Abstract
Convolutional neural networks (CNNs) and mammalian visual systems share architectural and information processing similarities. We leverage these parallels to develop an in-silico CNN model simulating diseases affecting the visual system. This model aims to replicate neural complexities in an experimentally controlled environment. Therefore, we examine object recognition and internal representations of a CNN under neurodegeneration and neuroplasticity conditions simulated through synaptic weight decay and retraining. This approach can model neurodegeneration from events like tau accumulation, reflecting cognitive decline in diseases such as posterior cortical atrophy, a condition that can accompany Alzheimer's disease and primarily affects the visual system. After each degeneration iteration, we retrain unaffected synapses to simulate ongoing neuroplasticity. Our results show that with significant synaptic decay and limited retraining, the model's representational similarity decreases compared to a healthy model. Early CNN layers retain high similarity to the healthy model, while later layers are more prone to degradation. The results of this study reveal a progressive decline in object recognition proficiency, mirroring posterior cortical atrophy progression. In-silico modeling of neurodegenerative diseases can enhance our understanding of disease progression and aid in developing targeted rehabilitation and treatments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".