Dynamics of neural networks over undirected graphs

Eric Goles, Gonzalo A. Ruz

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

3 Citas (Scopus)


In this paper we study the dynamical behavior of neural networks such that their interconnections are the incidence matrix of an undirected finite graph G=(V, E) (i.e., the weights belong to {0, 1}). The network may be updated synchronously (every node is updated at the same time), sequentially (nodes are updated one by one in a prescribed order) or in a block-sequential way (a mixture of the previous schemes). We characterize completely the attractors (fixed points or cycles). More precisely, we establish the convergence to fixed points related to a parameter α(G), taking into account the number of loops, edges, vertices as well as the minimum number of edges to remove from E in order to obtain a maximum bipartite graph. Roughly, α(G')<0 for any G' subgraph of G implies the convergence to fixed points. Otherwise, cycles appear. Actually, for very simple networks (majority functions updated in a block-sequential scheme such that each block is of minimum cardinality two) we exhibit cycles with non-polynomial periods.

Idioma originalInglés
Páginas (desde-hasta)156-169
Número de páginas14
PublicaciónNeural Networks
EstadoPublicada - 1 mar. 2015


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