A Dynamic Network Model for Nanoscale Systems

Much current research is concentrated on building computing networks based on nanoscale devices. However, there has been less progress in programming these networks to produce useful computation. This paper considers a model of computation based on Random Boolean Networks (RBNs). RBNs have the ability to form complex dynamic patterns in ways that produce collective information processing. This approach to information processing is very different from classical approaches, and requires non-standard synthesis and analysis techniques. A random network-based model seems to be appealing for emerging nanotechnologies in which it is difficult to control the growth direction or achieve precise assembly. We formally define the model, discuss its universality, and investigate an evolutionary method for ”programming” RBNs to perform computations.