Computation with spiking neurons takes advantage of the
abstraction of action potentials into streams of stereotypical events, which
encode information through their timing. This approach both reduces
power consumption and alleviates communication bottlenecks. A number
of such spiking custom mixed-signal address event representation
(AER) chips have been developed in recent years.
In this paper, we present i) a flexible event-driven platform consisting
of the integration of a visual AER sensor and the SpiNNaker system,
a programmable massively parallel digital architecture oriented to the
simulation of spiking neural networks; ii) the implementation of a neural
network for feature-based attentional selection on this platfor