Chaotic itinerancy, temporal segmentation and spatio-temporal combinatorial codes
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.
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