Abstract

Studies of how information is processed in natural systems, in particular in nervous systems, are rapidly gaining attention. Less known however is that the local dynamics of such information processing in space and time can be measured. In this chapter, we review the mathematics of how to measure local entropy and mutual information values at specific observations of time-series processes.We then review how these techniques are used to construct measures of local information storage and transfer within a distributed system, and we describe how these measures can reveal much more intricate details about the dynamics of complex systems than their more well-known “average” measures do. This is done by examining their application to cellular automata, a classic complex system, where these local information profiles have provided quantitative evidence for long-held conjectures regarding the information transfer and processing role of gliders and glider collisions. Finally, we describe the outlook in anticipating the broad application of these local measures of information processing in computational neuroscience.

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