title: Period-two cycles in a feedforward layered neural network model with symmetric sequence processing.
abstract: The effects of dominant sequential interactions are investigated in an exactly solvable feedforward layered neural network model of binary units and patterns near saturation in which the interaction consists of a Hebbian part and a symmetric sequential term. Phase diagrams of stationary states are obtained and a phase of cyclic correlated states of period two is found for a weak Hebbian term, independently of the number of condensed patterns c.
authors.affiliation: Instituto de Física, Universidade Federal do Rio Grande do Sul, Caixa Postal 15051, 91501-970 Porto Alegre, Brazil.
authors.forename: F L
authors.initials: FL
authors.lastname: Metz
authors.forename: W K
authors.initials: WK
authors.lastname: Theumann
mesh_terms: D000465:Algorithms; D000818:Animals; D001703:Biophysics / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D008956:Models, Chemical; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D013269:Stochastic Processes