Binary Pattern Discrimination
A static, globally observable binary classification task. The model receives a single pattern and must assign it to one of two classes. There is no temporal structure and no delay between stimulus and decision.
Results
Range marks show ±1 SD across seeds. All metrics (95% CI, parameter counts, seed counts, training times) are sourced from real NeuroWeave experiment artifacts. Click any row to expand.
Specification
A static, globally observable binary classification task. The model receives a single pattern and must assign it to one of two classes. There is no temporal structure and no delay between stimulus and decision.
Simple static global classification does not reveal a unique MaleCNS advantage — dense, random-graph and conventional baselines reach the same ceiling.