Before revision:  This principle permits to analytically work out an explicit and completely local plasticity rulesupporting the efficient solution of several tasks. 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:  This principle permits to analytically work out an explicit and biologically plausible plasticity rule. Here we propose a novel target-based learning scheme in which such a rule can be used to efficiently train a RSNN to solve several temporal tasks such as learning multidimensional trajectory and an implementation of the temporal XOR. We finally show that an online approximation of the likelihood maximization is extremely beneficial to fast learning .