  Recurrent Bistable Gradient Networks are attractor based neural networks
characterized by bistable dynamics of each single neuron. Coupled together
using linear interaction determined by the interconnection weights, these
networks do not suffer from spurious states or very limited capacity anymore.
Vladimir Chinarov and Michael Menzinger, who invented these networks, trained
them using Hebb's learning rule. We show, that this way of computing the
weights leads to unwanted behaviour and limitations of the networks
capabilities. Furthermore we evince, that using the first order of Hintons
Contrastive Divergence algorithm leads to a quite promising recurrent neural
network. These findings are tested by learning images of the MNIST database for
handwritten numbers.
