Reflexive Biodynamics for Immune Inspired Computing

h i f i c i a l hune Systems (A.S. Perelson, 1988; D. Dasgupto, 1998) is a rapidly growing field of information processing based upon immune inspired paradigms of nonlinear dynamics. Although it has many features in common with neural networks, there are some differences: the immune system is more complex, more diverse, and it performs many different functions simultaneously. In contradistinction to neural networks, the immune system, from the viewpoint of nonlinear dynamics, can be considered as a multi-body system (with “bodies ” represented by cells) which is interconnected via information flows. Since these flows as well as responses to them may be distorted, delayed, or incomplete, the motion of each cell becomes stochastic, and it can be simulated by a controlled random walk. One of the main challenges in modeling living systems is to distinguish a random walk of physical origin (for instance, Brownian motions) from those of biological origin and that will constitute the starting point of the proposed approach. As conjectured in 13], the biological random walk must be nonlinear. Indeed, any stochastic Markov process can be described by linear Fokker-Planck equation (or its discretized version) [41, only tha