Memory and Selectivity in Evolving Scale-Free Immune Networks

In this paper we examine the impact of graph theory and more particularly the scale-free topology on Immune Network models. In the case of a simple but not trivial model we analyze network performances as long term selectivity properties, its computational capabilities as memory capacity, and relation with Neural Networks. A more advanced Immune Network model is conceptualized and it is developed a scaffold for further mathematical investigation.