Complexity-based Encoded Information Quantification in Neurophysiological Recordings

TL;DR

This paper introduces a novel compression-based method grounded in algorithmic information theory to quantify the similarity and encoded information in neurophysiological responses, validated on synthetic and real data.

Contribution

It presents a new approach for directly quantifying similarity in brain responses using compression, improving upon existing information-theoretic methods.

Findings

Effective in discriminating between different brain states.

Outperforms mutual information in estimating encoded information.

Applicable to both synthetic and real neurophysiological data.

Abstract

Brain activity differs vastly between sleep, cognitive tasks, and action. Information theory is an appropriate concept to analytically quantify these brain states. Based on neurophysiological recordings, this concept can handle complex data sets, is free of any requirements about the data structure, and can infer the present underlying brain mechanisms. Specifically, by utilizing algorithmic information theory, it is possible to estimate the absolute information contained in brain responses. While current approaches that apply this theory to neurophysiological recordings can discriminate between different brain states, they are limited in directly quantifying the degree of similarity or encoded information between brain responses. Here, we propose a method grounded in algorithmic information theory that affords direct statements about responses' similarity by estimating the encoded information through a compression-based scheme. We validated this method by applying it to both synthetic and real neurophysiological data and compared its efficiency to the mutual information measure. This proposed procedure is especially suited for task paradigms contrasting different event types because it can precisely quantify the similarity of neuronal responses.