A new online unsupervised learning rule for the BSB model
Sylvain Chartier, Robert Proulx · 2002
In this paper it is demonstrated that a new unsupervised learning rule enable a nonlinear model, like the BSB model and the Hopfield network, to learn online correlated stimuli. This rule stabilizes the weight matrix growth to the projection rule in a local fashion. The model has been tested with computer simulations that show that the model is stable over the variations of its free parameters and that it is noise tolerant in the recall task.