V. N. Biktashev, A. M. Molchanov
1992
General features of a mathematical method of analyzing complex systems like neural networks are presented. The method is a generalization of the mean-field approach and allows analyzing not only "steady-states" but also dynamical properties of the network. The method can be also 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 method can treat synchronization processes, networks of excitable neurons and ones of identical neurons and synapses.
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