In this work, we develop a novel neuro-symbolic model for automated seizure detection using multi-views of data representation. Firstly, the spectral and line length features are extracted using a multi-view feature extraction technique. Next, a signal temporal logic neural network (STONE) that combines the benefits of neural networks and temporal logics is constructed to classify the seizure and nonseizure data. STONE is designed in such a way that each neuron has a symbolic representation corresponding to a component in a weighted signal temporal logic (wSTL) formula. Compared with traditional STL inference algorithms, STONE is end-to-end differentiable such that the learning can be accomplished through back-propagation. In addition, STONE improves the interpretability of seizure detection models as the outcome of STONE is a wSTL formula that is interpretable and human-readable. Importantly, the wSTL formula reveals the reasoning behind seizure as a description of the evolution of EEG signals. STONE is tested on two popular EEG databases and demonstrated to achieve promising detection performance in terms of accuracy, sensitivity, and specificity when compared with existing state-of-the-art models. Furthermore, STONE can provide a human-readable formula as a description of the seizure characteristics, and the formula is also visualizable for easy interpretation of the classifier, which is a missing property in existing seizure detection methods.