Local information transfer as a spatiotemporal filter for complex systems

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Abstract
We present a measure of local information transfer, derived from an existing averaged information-theoretical measure, namely, transfer entropy. Local transfer entropy is used to produce profiles of the information transfer into each spatiotemporal point in a complex system. These spatiotemporal profiles are useful not only as an analytical tool, but also allow explicit investigation of different parameter settings and forms of the transfer entropy metric itself. As an example, local transfer entropy is applied to cellular automata, where it is demonstrated to be a useful method of filtering for coherent structure. More importantly, local transfer entropy provides the first quantitative evidence for the long-held conjecture that the emergent traveling coherent structures known as particles (both gliders and domain walls, which have analogs in many physical processes) are the dominant information transfer agents in cellular automata.
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