Abstract
A study of the time evolution and a stability analysis of the phases in the extremely diluted Blume-Emery-Griffiths neural network model are shown to yield new phase diagrams in which fluctuation retrieval may drive pattern retrieval. It is shown that saddle-point solutions associated with fluctuation overlaps slow down the flow of the network states towards the retrieval fixed points. A comparison of the performance with other three-state networks is also presented.
Contido em
Physical review. E, Statistical, nonlinear, and soft matter physics. Vol. 68, no. 6 (Dec. 2003), 062901, 4 p.
