Assimilating and integrating network signals for solving some complex problems with a multiscale neural architecture
In nature, some phenomena are so complicated (complex) that they are difficult to understand or to analyze in a formal manner, due to a great deal of uncertainties caused by inevitable noises. In this paper, we propose an open evolutionary architecture that has a great flexibility in representing information, and that possesses a rich potential for the evolution of a variety of behaviors that could significantly expand the problem domains to which neural computing is applicable. The proposed model is a mockup (abstract) system that consists of ambient grids and living nodes, which may interact with each other via various kinds of communication mechanisms. Experiment result shows that it takes advantages of system dynamics occurring in different levels to accomplish assigned tasks. It also shows the system exhibits certain degrees of robustness in dealing with noises generated in the environments.
complex phenomena mockup system neural dynamics evolutionary learning