  We propose a model of an adaptive network of spiking neurons that gives rise
to a hypernetwork of its dynamic states at the upper level of description. Left
to itself, the network exhibits a sequence of transient clustering which
relates to a traffic in the hypernetwork in the form of a random walk.
Receiving inputs the system is able to generate reproducible sequences
corresponding to stimulus-specific paths in the hypernetwork. We illustrate
these basic notions by a simple network of discrete-time spiking neurons
together with its FPGA realization and analyze their properties.
