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A growing body of empirical evidence suggests that neuronal and biochemical networks are oftencharacterized by long transients which are quasi-stable, with fast switching between them. Theduration of the quasi-stable patterns is much longer than one would expect from the characteristicelementary processes of the system, whereas the switching is triggered by external signals orsystem-autonomously, and occurs on a timescale much shorter than the one of the precedingdynamical pattern. Generalizing the concept of ghost states, we provide a theoretical frameworkthat accounts for emergence of transiently stable phase-space flows generated by ghostscaffolds. We demonstrate that ghost-based phase space objects such as ghost channels andcycles account for emergence of advanced information processing capabilities, including timeinterval prediction tasks.