Cellular nonlinear networks are naturally inspired computing architectures where complex dynamic behaviors may emerge as a result of the local or prescribed connectivity among simple cells. Functionally, much like in biology, each cell is defined by a few bits of information called a gene. Such systems may be used in signal processing applications (intelligent sensors) or may be used to model and understand natural systems. While many publications focus on the dynamics in cellular automata and various applications, less deal with an important problem, that of designing for emergence. Put in simple words: How to choose a cell such that a desired behavior will occur in the cellular system.

This book proposes a systematic framework for measuring emergence and a systematic design method to locate computationally meaningful genes in a reasonable computing time. Programs and application examples are provided so that the reader may easily understand the new concepts and develop her own specific experiments. An accessible language recommends it to a large audience including specialists from various interdisciplinary fields who may benefit from a better understanding of emergence and its applications to their specific field.

Introduction to emergent computation in cellular nonlinear networks With applications in natural computing and signal processing Includes supplementary material: sn.pub/extras

Inhalt
Natural Computing Paradigms and Emergent Computation.- Cellular Nonlinear Networks: State of the Art and Applications.- Cellular and Natural Computing Models and Software Simulation.- Emergence, Locating and Measuring It.- Exponents of Growth.- Sieves for Selection of Genes According to Desired Behaviors.- Predicting Emergence from Cell's Structure.- Applications of Emergent Phenomena.