AI + BIOLOGY + REPRODUCIBLE COMPUTING
NeuroFly
A research sandbox for testing how AI could detect, predict and compensate for functional decline in Drosophila neurodegeneration models.
Current public demo uses a deterministic synthetic model. NF-001 is an animated agent driven by experiment state; it is not a living organism or a biological brain.
neurofly@lab:~$ boot --agent NF-001
✓ model neurofly-synthetic-v0.1 loaded
✓ chamber boundaries active
state: exploring
experiment: early-detection
signal: baseline
SENSOR FIELD
BOUNDARY
runtime active
00:00:00
EXPERIMENT REGISTRY
Questions before claims
simulation
early-detection
Early Neurodegeneration Detection
Can longitudinal behavioral signals reveal a synthetic disease trajectory before a conventional threshold is crossed?
simulation
prospective-risk
Prospective Progression Prediction
Can an early behavioral window predict later synthetic functional decline?
simulation
adaptive-compensation
AI Adaptive Compensation
Can a closed-loop controller reduce task errors in a synthetic impaired agent without changing the disease model?
LIVE SYNTHETIC SANDBOX
Run a reproducible trajectory
synthetic result
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RESEARCH ROADMAP
From sandbox to defensible science
- Phase 0 — Synthetic validation. Exercise the architecture, provenance, APIs and visualization without pretending the values are biological.
- Phase 1 — Public datasets. Train and evaluate behavioral models on published Drosophila tracking datasets with explicit provenance.
- Phase 2 — External lab collaboration. Prospectively test early-detection hypotheses in an established neurodegeneration model under appropriate institutional oversight.
- Phase 3 — Closed-loop assistance. Evaluate whether adaptive sensory feedback changes task performance, clearly separating compensation from treatment.