DocumentCode
1577565
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
fYear
1992
Firstpage
948
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Neuroinformatics and Neurocomputers, 1992., RNNS/IEEE Symposium on
Conference_Location
Rostov-on-Don
Print_ISBN
0-7803-0809-3
Type
conf
DOI
10.1109/RNNS.1992.268535
Filename
268535
Link To Document