Before revision:  We show that the learning algorithm can be achieved in a very few iterations, and that the online approximation of the likelihood maximization is extremely beneficial to fast learning .
After revision:  We show that the learning algorithm can be achieved in a very few iterations, and that the online approximation of the gradient ascent, in addition to guaranteeing complete locality in time and space, allows learning after very few presentations of the target output .