Formalizing neural networks using graph transformations

In this paper a unifying framework for the formalization of different types of neural networks and the corresponding algorithms for computation and training is presented. The used graph transformation system offers a formalism to verify properties of the networks and their algorithms. In addition the presented methodology can be used as a tool to visualize and design different types of networks along with all required algorithms. An algorithm that adapts network parameters using standard gradient descent as well as parts of a constructive, topology-changing algorithm for probabilistic neural networks are used to demonstrate the proposed formalism