  Artificial spike-based computation, inspired by models of computation in the
central nervous system, may present significant performance advantages over
traditional methods for specific types of large scale problems. This paper
describes very simple network architectures for k-winners-take-all and
soft-winner-take-all computation using neural oscillators. Fast convergence is
achieved from arbitrary initial conditions, which makes the networks
particularly suitable to track time-varying inputs.
