Self-control in Sparsely Coded Networks
arXiv:cond-mat/9801273 · doi:10.1103/PhysRevLett.80.2961
Abstract
A complete self-control mechanism is proposed in the dynamics of neural networks through the introduction of a time-dependent threshold, determined in function of both the noise and the pattern activity in the network. Especially for sparsely coded models this mechanism is shown to considerably improve the storage capacity, the basins of attraction and the mutual information content of the network.
4 pages, 6 Postscript figures