PUB - Publikationen an der Universität Bielefeld

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.