Binary Spiking Neural Networks as Causal Models
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 th...
| Authors | Aditya Kar, Emiliano Lorini, Timothée Masquelier |
|---|---|
| Primary category | cs.AI |
| Published | 29 April 2026 |
| Last updated at source | 3 June 2026 |
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