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Brain activity differs vastly between sleep, cognitive tasks, and action.Information theory is an appropriate concept to analytically quantify thesebrain states. Based on neurophysiological recordings, this concept can handlecomplex data sets, is free of any requirements about the data structure, andcan infer the present underlying brain mechanisms. Specifically, by utilizingalgorithmic information theory, it is possible to estimate the absoluteinformation contained in brain responses. While current approaches that applythis theory to neurophysiological recordings can discriminate between differentbrain states, they are limited in directly quantifying the degree of similarityor encoded information between brain responses. Here, we propose a methodgrounded in algorithmic information theory that affords direct statements aboutresponses' similarity by estimating the encoded information through acompression-based scheme. We validated this method by applying it to bothsynthetic and real neurophysiological data and compared its efficiency to themutual information measure. This proposed procedure is especially suited fortask paradigms contrasting different event types because it can preciselyquantify the similarity of neuronal responses.

Complexity-based Encoded Information Quantification in Neurophysiological Recordings