Binary spiking neural networks are modeled as binary causal models from which SAT and SMT solvers extract abductive explanations that provably exclude completely irrelevant input features.
Binary Spiking Neural Networks as Causal Models
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abstract
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.
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2026 1verdicts
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Binary Spiking Neural Networks as Causal Models
Binary spiking neural networks are modeled as binary causal models from which SAT and SMT solvers extract abductive explanations that provably exclude completely irrelevant input features.