Modeling brain information flow dynamics with multidimensional fuzzy inference systems

Abstract

In this paper, we introduce a pioneering approach to analyzing brain information flow dynamics through the development of a novel hybrid system. The key innovation of our system lies in the integration of n-cell fuzzy functions into a multidimensional fuzzy inference system (MFIS). Fuzzy n-cell numbers, representing a significant advancement in fuzzy mathematics, are central to our methodology, showcasing their capability to handle intricate uncertainties and imprecise data within multidimensional scenarios. Our proposed system aligns seamlessly with neuroscience principles, providing a robust framework to unravel the complexities of brain information flow dynamics. We leverage Transfer Entropy and Granger Causality measures for a quantitative assessment of influence strengths, enabling a systematic exploration of temporal information flow within the brain. Furthermore, the utilization of fuzzy n-cell numbers enhances the system's adaptability to the intricacies of neural processes. The outcomes of our study are presented through a series of visualizations, offering a comprehensive demonstration of the effectiveness and adaptability of the proposed hybrid system. This article aims to contribute to the understanding of brain dynamics by presenting a versatile and complex methodology that combines fuzzy mathematics, neuroscience principles, and advanced analytical techniques.