  This paper introduces a spiking hierarchical model for object recognition
which utilizes the precise timing information inherently present in the output
of biologically inspired asynchronous Address Event Representation (AER) vision
sensors. The asynchronous nature of these systems frees computation and
communication from the rigid predetermined timing enforced by system clocks in
conventional systems. Freedom from rigid timing constraints opens the
possibility of using true timing to our advantage in computation. We show not
only how timing can be used in object recognition, but also how it can in fact
simplify computation. Specifically, we rely on a simple
temporal-winner-take-all rather than more computationally intensive synchronous
operations typically used in biologically inspired neural networks for object
recognition. This approach to visual computation represents a major paradigm
shift from conventional clocked systems and can find application in other
sensory modalities and computational tasks. We showcase effectiveness of the
approach by achieving the highest reported accuracy to date (97.5\%$\pm$3.5\%)
for a previously published four class card pip recognition task and an accuracy
of 84.9\%$\pm$1.9\% for a new more difficult 36 class character recognition
task.
