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arXiv · 0704.2580

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

F. L. Metz·W. K. Theumann
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Abstract

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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F. L. Metz, W. K. Theumann. 2007-04-19. Period-two cycles in a feed-forward layered neural network model with symmetric sequence processing. https://doi.org/10.1103/physreve.75.041907

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