Relating Bayesian learning to training in recurrent networks

It will be demonstrated that a recurrent neural network relying on an error correcting (truncated gradient descent) 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.