Cellular Neural Networks for Realizing Associative Memories

The classical modeling approach to prey-predator systems relies on extensions of Lotka-Volterra differential equations. In this paper a Cellular Automata-based model is proposed as an alternative approach for the simulation of a two population ecosystem of mites. It is shown how several complex features affecting mite population evolution, such as the egg disclosure time, the sexual maturation time, the limited life time, the limited survival capability of predators in fasting condition, and juvenile mortality, can be embedded in the Cellular Automata framework. Preliminary simulation results, together with a comparison between data of a real experiment and data from simulation experiments, are reported. Experimental data are fitted quite well by predictions of the Cellular Automata-based model, whereas they are not by results from the analytical Lotka-Volterra model.