M. O. Sevinc

Continuous attribution on discrete spikes: revealing the explainability paradox in spiking neural networks

Spiking neural networks (SNNs) offer a highly energy-efficient, neuromorphic alternative to standard deep learning, but their real-world deployment necessitates trustworthy and explainable outputs. Class Activation Mapping (CAM) methods are a robust and popular way of implementing this explainability in continuous arti...