Title :
A macrodynamical approach to the analysis of neural networks
Author :
Biktashev, V.N. ; Molchanov, A.M.
Author_Institution :
Inst. of Math. Problems of Biol., Moscow, Russia
Abstract :
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
Keywords :
biocybernetics; finite element analysis; master equation; neural nets; variational techniques; Galerkin procedure; asymptotical method; dynamical properties; excitable neurons; master equation; mean-field approach; neural networks; steady states; synchronization processes; Biology computing; Lattices; Mathematical model; Microscopy; Nearest neighbor searches; Neural networks; Neurons; Partial differential equations; Steady-state;
Conference_Titel :
Neuroinformatics and Neurocomputers, 1992., RNNS/IEEE Symposium on
Conference_Location :
Rostov-on-Don
Print_ISBN :
0-7803-0809-3
DOI :
10.1109/RNNS.1992.268535
