Integrated information theory and its application for analysis of brain neuron activity

Abstract

Purpose of this review is to consider the possibility to apply the integrated information theory to investigate the brain neural activity. Earlier was shown that the integrated information amount Φ quantifies a degree of a dynamic complexity of a system and able to predict a level of its success defined by classic observable benchmarks. For this reason, a question arises about the application of the integrated information theory to analyse changes in brain spiking activity due the acquisition of new experience. Conclusion. The bases of the integrated information theory and its possible application in neurobiology to investigate the process of new experience acquisition were reviewed. It was shown that the amount of integrated information Φ is a metric which is able to quantify the dynamic complexity of brain neural networks increasing when the new experience is acquired. Methods, enaabling the practical calculation of Φ for spiking data, were proposed.