  Neural networks currently dominate the machine learning community and they do
so for good reasons. Their accuracy on complex tasks such as image
classification is unrivaled at the moment and with recent improvements they are
reasonably easy to train. Nevertheless, neural networks are lacking robustness
and interpretability. Prototype-based vector quantization methods on the other
hand are known for being robust and interpretable. For this reason, we propose
techniques and strategies to merge both approaches. This contribution will
particularly highlight the similarities between them and outline how to
construct a prototype-based classification layer for multilayer networks.
Additionally, we provide an alternative, prototype-based, approach to the
classical convolution operation. Numerical results are not part of this report,
instead the focus lays on establishing a strong theoretical framework. By
publishing our framework and the respective theoretical considerations and
justifications before finalizing our numerical experiments we hope to
jump-start the incorporation of prototype-based learning in neural networks and
vice versa.
