Different Approximation Methods for Calculation of Integrated Information Coefficient in the Brain during Instrumental Learning

The amount of integrated information, [Formula: see text] , proposed in an integrated information theory (IIT) is useful to describe the degree of brain adaptation to the environment. However, its computation cannot be precisely performed for a reasonable time for time-series spike data collected from a large count of neurons.. Therefore, [Formula: see text] was only used to describe averaged activity of a big group of neurons, and the behavior of small non-brain systems. In this study, we reported on ways for fast and precise [Formula: see text] calculation using different approximation methods for Φ calculation in neural spike data, and checked the capability of [Formula: see text] to describe a degree of adaptation in brain neural networks. We show that during instrumental learning sessions, all applied approximation methods reflect temporal trends of [Formula: see text] in the rat hippocampus. The value of [Formula: see text] is positively correlated with the number of successful acts performed by a rat. We also show that only one subgroup of neurons modulates their Φ during learning. The obtained results pave the way for application of [Formula: see text] to investigate plasticity in the brain during the acquisition of new tasks.