  Verifying properties and interpreting the behaviour of deep neural networks
(DNN) is an important task given their ubiquitous use in applications,
including safety-critical ones, and their black-box nature. We propose an
automata-theoric approach to tackling problems arising in DNN analysis. We show
that the input-output behaviour of a DNN can be captured precisely by a
(special) weak B\"uchi automaton and we show how these can be used to address
common verification and interpretation tasks of DNN like adversarial robustness
or minimum sufficient reasons.
