  We study a deterministic dynamics with two time scales in a continuous state
attractor network. To the usual (fast) relaxation dynamics towards point
attractors (``patterns'') we add a slow coupling dynamics that makes the
visited patterns to loose stability leading to an itinerant behavior in the
form of punctuated equilibria. One finds that the transition frequency matrix
between patterns shows non-trivial statistical properties in the chaotic
itinerant regime. We show that mixture input patterns can be temporally
segmented by the itinerant dynamics. The viability of a combinatorial
spatio-temporal neural code is also demonstrated.
