A macrodynamical approach to the analysis of neural networks

General features of an asymptotical method for an analyzing complex system like neural networks are presented. The method is analogous to the mean-field approach and allows treatment not only of steady states but also of dynamical properties of networks. It may also be interpreted as a Galerkin procedure for the master equation. The types of neural networks and related problems to which the method can be applied are discussed. It is shown that the method can treat synchronization processes, networks of excitable neurons, nonidentical neurons, and nonidentical synapses.<
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