Relating Bayesian learning to training in recurrent networks
Rainer Spiegel · 2004
It is demonstrated that a recurrent neural network relying on an error correcting learning algorithm and a localist coding scheme is able to converge to a solution that would be expected from Bayesian learning. This is possible even without implementing Bayes theorem and without assigning prior probabilities to the model.