 We use a general probabilistic framework that relies on the principle of maximizing the likelihood for the network to reproduce some temporal dynamics. 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 taskssuch as learning multidimensional trajectory and an implementation of the temporal XOR.
<clarity>  We use a general probabilistic framework that relies on the principle of maximizing the likelihood for the network to reproduce some temporal dynamics. 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 taskssuch as learning multidimensional trajectory and an implementation of the temporal XOR.
 We use a general probabilistic framework that relies on the principle of maximizing the likelihood for the network to solve a specific task. 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 taskssuch as learning multidimensional trajectory and an implementation of the temporal XOR.