Binary Spiking Neural Networks as Causal Models
Provides a novel method for generating provably minimal explanations in BSNNs, addressing the need for faithful interpretability in neuromorphic computing.
The authors propose a causal model for Binary Spiking Neural Networks (BSNNs) and use SAT/SMT solvers to compute abductive explanations that guarantee no irrelevant features, outperforming SHAP on MNIST.
We provide a causal analysis of Binary Spiking Neural Networks (BSNNs) to explain their behavior. We formally define a BSNN and represent its spiking activity as a binary causal model. Thanks to this causal representation, we are able to explain the output of the network by leveraging logic-based methods. In particular, we show that we can successfully use a SAT as well as a SMT solver to compute abductive explanations from this binary causal model. To illustrate our approach, we trained the BSNN on the standard MNIST dataset and applied our SAT-based and SMT-based methods to finding abductive explanations of the network's classifications based on pixel-level features. We also compared the found explanations against SHAP, a popular method used in the area of explainable AI. We show that, unlike SHAP, our approach guarantees that a found explanation does not contain completely irrelevant features.