1. A method for computer-aided learning of a recurrent neural network for modeling a dynamic system which is characterized at respective times by an observable vector comprising one or more observables as entries, the method comprising: providing a recurrent neural network comprising a causal network, a retro-causal network, an observable vector with one or more observables, wherein the causal network describes an information flow proceeding forward in time between first state vectors of the dynamic system, wherein a first state vector at a respective point in time comprises one or more first entries which are each assigned to an entry of the observable vector, and one or more hidden states of the dynamic system, wherein the retro-causal network describes an information flow proceeding backward in time between second state vectors of the dynamic system, wherein a second state vector at a respective point in time comprises one or more second entries which are each assigned to an entry of the observable vector, and one or more hidden states of the dynamic system, determining the observable vector by respectively combining the first entries of the first state vector with the second entries of the second state vector for respective points of time before and after a current point of time, wherein the causal network and the retro-causal network are learned based on training data which contains a sequence of consecutive known observable vectors.