An increasing number of research groups develop dedicated hybrid analog/digital very large scale integration (VLSI) devices implementing hundreds of spiking neurons with bio--physically realistic dynamics. However, despite the significant progress in their design, there is still little insight in translating circuitry of neural assemblies into desired (non-trivial) function. In this work, we propose to use neural circuits implementing the soft Winner--Take--All (WTA) function. By showing that recurrently connected instances of them can have persistent activity states, which can be used as a form of working memory, we argue that such circuits can perform state--dependent computation. We demonstrate such a network in a distributed neuromorphic system consisting of two multi--neuron chips implementing soft WTA, stimulated by an event--based vision sensor. The resulting network is able to track and remember the position of a localized stimulus along a trajectory previously encoded in the system.