arXiv:2604.27007v2 Announce Type: replace 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
The increasing complexity and opacity of neural networks necessitates explainability, and this research leverages logic-based methods for Binary Spiking Neural Networks.
This work offers a clear pathway to understanding the decision-making process within a class of neural networks, which is critical for trust and adoption in sensitive applications.
The ability to formally explain BSNN outputs using causal models and SAT/SMT solvers provides a novel tool for network verification and debugging, potentially accelerating their real-world deployment.
- · AI explainability researchers
- · Developers of spiking neural networks
- · Sectors requiring audited AI (e.g., finance, defense)
- · Black-box AI approaches in critical applications
Improved debugging and verification of certain neural network architectures facilitate their design and application.
Increased trust in AI systems due to enhanced transparency could accelerate AI adoption in regulated industries.
The methodology could inspire similar causal analysis frameworks for other, more complex AI models, leading to a broader shift towards explainable AI.
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Read at arXiv cs.AI