Complex systems theory is a nebulous field whose overarching goal is to understand the dynamical behavior of systems consisting of many interconnected component parts. It has attracted widespread interest from many domains that study examples of such systems, including ecologists, sociologists, engineers, artificial intelligence researchers, condensed matter physicists, neuroscientists, and many others. The results of these collected, multi-disciplinary efforts have not been so much a comprehensive theory of Complex Systems (capital-C, capital-S), but rather a set of techniques, analogies, and attitudes toward problem solving that emphasize interactions and dynamics over individual components and their functions. The chapters are written in a complex adaptive systems frame and therefore it is useful to provide a provisional theoretical description of such systems. Following Holland [1], a generalizable description of complex adaptive systems is that they are collections of relatively simple agents that have the property that they can aggregate, so that collections of agents can form meta-agents (and meta-meta-agents etc.) with higher-order structure. These aggregates interact nonlinearly, so that the aggregate behavior of a collection of agents is qualitatively different from the behavior of the individual agents. The interactions among agents mediate flows of materials or information. Finally, the agents are typically diverse with distinct specialties that are optimized through adaptation to selective pressures in their environments.

The previous chapters have dealt with the complex adaptive nature of the genome. Similar concepts in terms of interacting elements, self-organization and adaptation can be applied at other hierarchical scales. In this chapter we will show how complex adaptive systems (CAS) concepts can be usefully applied at the level of action potential firing patterns of single neurons in terms of seizure generation and of associated morbidities.