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

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 $c$.

8 pages and 5 figures

References in corpus (5)

• Transient dynamics for sequence processing neural networks
• The path-integral analysis of an associative memory model storing an infinite number of finite limit cycles
• Mean-field dynamics of sequence processing neural networks with finite connectivity
• Retrieval Properties of Hopfield and Correlated Attractors in an Associative Memory Model
• Pattern reconstruction and sequence processing in feed-forward layered neural networks near saturation

Cited by in corpus (1)

• Symmetric sequence processing in a recurrent neural network model with a synchronous dynamics