Period-two cycles in a feed-forward layered neural network model with symmetric sequence processing

Details Period-two cycles in a feed-forward layered neural network model with symmetric sequence processing Period-two cycles in a feed-forward layered neural network model with symmetric sequence processing

2007-04-19

arxiv.org/abs/0704.2580v1

Phys. Rev. E 75, 041907 (2007)

Physics

Condensed Matter

Disordered Systems and Neural Networks

8 pages and 5 figures

Scientific paper

The effects of dominant sequential interactions are investigated in an exactly solvable feed-forward 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 new phase of cyclic correlated states of period two is found for a weak Hebbian term, independently of the number of condensed patterns $c$.

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