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 recur-

rently 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.