Preprint Article Version 1 This version is not peer-reviewed

A Measure of Information Available for Prediction

Version 1 : Received: 2 November 2017 / Approved: 2 November 2017 / Online: 2 November 2017 (15:14:40 CET)
Version 2 : Received: 11 May 2018 / Approved: 11 May 2018 / Online: 11 May 2018 (06:24:06 CEST)

How to cite: Isomura, T. A Measure of Information Available for Prediction. Preprints 2017, 2017110020 (doi: 10.20944/preprints201711.0020.v1). Isomura, T. A Measure of Information Available for Prediction. Preprints 2017, 2017110020 (doi: 10.20944/preprints201711.0020.v1).

Abstract

Mutual information between the brain state and the external world state represents the amount of information stored in the brain that is associated with the external world. On the other hand, surprise of sensory input indicates the unpredictability of the current input. In other words, this is a measure of prediction capability, and an upper bound of surprise is known as free energy. According to the free-energy principle (FEP), the brain continues to minimize free energy to perceive the external world. For animals to survive, prediction capability is considered more important than just memorizing information. In this study, the fact that free energy represents a gap between the amount of information stored in the brain and that available for prediction is established, where the latter will be referred to as predictive information as an analogy with Bialek's predictive information. This concept involves the FEP, the infomax principle, and the predictive information theory, and will be a useful measure to quantify the amount of information available for prediction.

Subject Areas

the free-energy principle; internal model hypothesis; unconscious inference; infomax principle; predictive information; independent component analysis; principal component analysis

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