Generalized Learning Vector Quantization methods are a powerful and robust approach for classification tasks. They compare incoming samples with representative prototypes for each target class. While prototypes are physically interpretable, they do not account for changes in the environment. We propose a novel framework for the incorporation of context information into prototype generation. We can model dependencies in a modular way ranging from polynomials to neural networks. Evaluations on artificial and real-world datasets show an increase in performance and meaningful prototype adaptations.