Self-organization in computer simulated selective systems

In certain biologic systems, the signal selects functional or numerical expansion of the recognizing elements. Examples of these systems include the immune system, brain cortex, and evolution. One common feature of these Darwinian-type systems is degenerate recognition, in which one signal can recognize several different elements, with different affinities and consequences. For example, T cell antigen receptors and antibodies demonstrate relative but not absolute specificity of recognition. Thus, the variables of dose of the signal and affinity of the recognizing element modulate the outcome. Another feature of these systems is the ability to create self-organized patterns, which do not mirror the incoming signals. The hypothesis of this study is that degenerate recognition with subsequent selection of recognizing elements can explain self-organization of these systems. An entirely numerical model was explored, using the cellular automata approach. Three intrinsic features of a common selective system were incorporated into this model: a large number of recognizing elements; degenerative recognition of stimuli by these elements; and subsequent selection. Different numerical patterns of incoming stimuli were tested. The model showed self-organizing dynamics. Usually, the population of recognizing elements demonstrated an initial period of equilibrium, then a chaotic transitional state, and, finally, the bifurcational appearance of a stable self-organized pattern. The final resolution into a stable pattern can be either gradual or quasi-saltational. I conclude that systems with a large number of recognizing elements, degenerative recognition, and selection of recognizing elements can self organize based upon the pattern of the incoming stimuli.