The human brain is a biological machine capable of performing real time computing tasks with an extremely reduced power budget. For this reason, brain inspired computing aims at mimicking the brain functioning to design new paradigms of computation. This requires to concurrently work at the hardware and at the software level, toward the optimization of their interplay. On the software side, a well established neuronal model is the Additive Model, that treats the brain as a non-linear dynamical system evolving under the influence of external stimuli. This model is classifiable as a continuous time Recurrent Neural Network (RNN). Recently, Bengio and Scellier have designed a learning algorithm named Equilbrium Propagation (EP) capable of efficiently performing learning on this model.