Period-two cycles in a feed-forward layered neural network model with symmetric sequence processing
Disordered Systems and Neural Networks
2015-05-13 v1
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 .
Keywords
Cite
@article{arxiv.0704.2580,
title = {Period-two cycles in a feed-forward layered neural network model with symmetric sequence processing},
author = {F. L. Metz and W. K. Theumann},
journal= {arXiv preprint arXiv:0704.2580},
year = {2015}
}