title: Time evolution of the extremely diluted Blume-Emery-Griffiths neural network.
abstract: A study of the time evolution and a stability analysis of the phases in the extremely diluted Blume-Emery-Griffiths neural network model are shown to yield new phase diagrams in which fluctuation retrieval may drive pattern retrieval. It is shown that saddle-point solutions associated with fluctuation overlaps slow down the flow of the network states towards the retrieval fixed points. A comparison of the performance with other three-state networks is also presented.
authors.affiliation: Instituut voor Theoretische Fysica, Katholieke Universiteit Leuven, B-3001 Leuven, Belgium.
authors.forename: D
authors.initials: D
authors.lastname: Bollé
authors.forename: D R C
authors.initials: DR
authors.lastname: Dominguez
authors.forename: R
authors.initials: R
authors.lastname: Erichsen
authors.forename: E
authors.initials: E
authors.lastname: Korutcheva
authors.forename: W K
authors.initials: WK
authors.lastname: Theumann
mesh_terms: D055592:Biophysical Phenomena; D001703:Biophysics; D015233:Models, Statistical; D016571:Neural Networks, Computer; D013997:Time Factors