  The fields of neural computation and artificial neural networks have
developed much in the last decades. Most of the works in these fields focus on
implementing and/or learning discrete functions or behavior. However,
technical, physical, and also cognitive processes evolve continuously in time.
This cannot be described directly with standard architectures of artificial
neural networks such as multi-layer feed-forward perceptrons. Therefore, in
this paper, we will argue that neural networks modeling continuous time are
needed explicitly for this purpose, because with them the synthesis and
analysis of continuous and possibly periodic processes in time are possible
(e.g. for robot behavior) besides computing discrete classification functions
(e.g. for logical reasoning). We will relate possible neural network
architectures with (hybrid) automata models that allow to express continuous
processes.
