Bidirected Information Flow in the High-Level Visual Cortex

Abstract

Understanding the brain function requires investigating information transfer across brain regions. Shannon began the remarkable new field of information theory in 1948. It basically can be divided into two categories: directed and undirected information-theoretical approaches. As we all know, neural signals are typically nonlinear and directed flow between brain regions. We can use directed information to quantify feed-forward information flow, feedback information, and instantaneous influence in the high-level visual cortex. Moreover, neural signals have bidirectional information flow properties and are not captured by the transfer entropy approach. Therefore, we used directed information to quantify bidirectional information flow in this study. We found that there has information flow between the scene-selective areas, e.g., OPA, PPA, RSC, and object-selective areas, e.g., LOC. Specifically, strong information flow exists between RSC and LOC. It explained that functionally coupled between RSC and LOC plays a vital role in visual scenes/object categories or recognition in our daily lives. Meanwhile, we also found weak reverse-directed information flow in the visual scenes and objects neural networks.