Neural networks have attracted much attention lately as a powerful tool of automatic learning. Of particular interest is the class of recurrent networks which allow for loops and cycles and thus give rise to dynamical systems, to flexible behavior, and to computation. This paper reviews the recent findings that mathematically quantify the computational power and dynamic capabilities of recurrent neural networks. The appeal of the network as a possible standard model of analog computation also will be discussed.