Processing structured data with recurrent neural networks

We will deal with the connection of symbolic and subsymbolic systems. The focus lies on the question of how is it possible to process symbolic data with neural networks. In so doing we examine the in principle capability of representing and learning symbolic data with various neural architectures which constitute partially dynamic approaches: discrete time partially recurrent neural networks as a simple and well established model for processing sequences, and advanced generalizations for processing tree structured data. Methods like holographic reduced representation, binary spatter codes, recursive autoassiociative memory, and folding networks share the in principle dynamics of how symbolic data are processed, whereas they differ in the specific training methods. We consider the following questions: Which are the representational capabilities of the architectures? Are the involved problems learnable in an appropriate sense? Are they efficiently learnable?