Information flow and emergence of the global patterns in connected neural networks

In a global network, which consists of many connected local networks, the self-organization processes are studied. The coupling between networks is taken into account in addition to the coupling between neurons. As dynamics of neural network two cases are treated: (1) the chaotic Brownian network and (2) Chua–Yang model of CNN. In the first case it is found that various kinds of global patterns appear in the global system, if some patterns, which are selected autonomously by the system itself, are stored in local networks. The global patterns are generated in terms of two patterns stored in local networks as the background and the figure. In the second case it is investigated how the pattern (or information) stored in a local network propagates in the global network and how the stationary pattern of the global network depends on the coupling between local networks. The relation between dynamics of the local network and the emergence of global patterns in the global network is discussed.