This book provides a powerful tool for collecting and correlating related bodies of research in modelling control and processing in distributed networks. While traditional publications in the field of network models have focussed on specific areas, this successfully intersects many related fields. These cover: control processes, modelling features and operations of biological neural networks and neurons, simulation of biological experimentation, and representation of artificial neural networks (ANNs) Within the fields mentioned, the topics discussed include: control solutions using theoretical computational learning models, learning algorithms and polynomial networks; simulating biological experimentation and physical mechanisms with computer-assisted and hardware models of biological neural networks and neurons; improving processes for representing artificial neural networks by verification from SPICE and global optimization techniques.