Research9h ago
Causal Analysis Explains Binary Spiking Neural Networks
Researchers represented Binary Spiking Neural Networks as binary causal models to decode their internal behavior. Using SAT and SMT solvers, the team extracted abductive explanations for MNIST classifications based on pixel-level features. This logic-based approach offers a more transparent alternative to SHAP. It provides practitioners a formal method for auditing spiking network decisions.