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
By Aditya Kar (CNRS, IRIT), Emiliano Lorini (CNRS, IRIT), Timoth\'ee Masquelier (CNRS, CERCO UMR5549)