Topic: transfer entropy

Understanding the causal connectivity within a network is crucial for unraveling its functional dynamics. However，the inferred causal connections are fundamentally influenced by the choice of causality measure employed, which may not always align with the actual structural connectivity of the network. The relationship between causal and structural connectivity, especially how different causality measures affect the inferred causal links, requires further exploration. In this talk, we examine nonlinear networks characterized by pulse signal outputs, such as spiking neural networks, using four p

The visual system processes vast amounts of information by internally weighting stimulus inputs based on their local features and behavioral relevance [1]. Although evidence shows that behavioral relevance influences stimulus saliency, it is not well understood how integrating stimulus and social information impacts perception and decision-making. Previous studies indicate that predicting others’ actions improves with real-time observation of their movements [3]. Additionally, game-theoretic simulations show that real-time feedback leads to leader-follower behavior, where one agent acts and t

Accessing structural connectivity information is crucial for understanding intricate functions of complex networks. In situations where direct measurement of structural connectivity is limited, like in cortical neuronal networks, causal inference tools such as transfer entropy are employed to reconstruct structural connectivity from network activity data. However, the causal connectivity inferred may not always align with the network's underlying structural connectivity, leaving the interpretation of inferred causal connectivity open for further clarification. Specifically, the relationship be