Consolidating a Link Centered Neural Connectivity Framework with Directed Transfer Function Asymptotics
We present a unified mathematical derivation of the asymptotic behaviour of three of the main forms of \textit{directed transfer function} (DTF) complementing recent partial directed coherence (PDC) results \cite{Baccala2013}. Based on these results and numerical examples we argue for a new directed `link' centered neural connectivity framework to replace the widespread correlation based effective/functional network concepts so that directed network influences between structures become classified as to whether links are \textit{active} in a \textit{direct} or in an \textit{indirect} way thereby leading to the new notions of \textit{Granger connectivity} and \textit{Granger influenciability} which are more descriptive than speaking of Granger causality alone.
Code (0)
등록된 구현이 없습니다.
Tasks
DescriptiveSimilar Papers 제목 키워드 기반
Revealing directed effective connectivity of cortical neuronal networks from measurements
In the study of biological networks, one of the major challenges is to understand the relationships between network structure and dynamics. In this paper, we model in vitro cortical neuronal cultures as stochastic dynami…
Functional ConnectivityBACE: Behavior-Adaptive Connectivity Estimation for Interpretable Graphs of Neural Dynamics
Understanding how distributed brain regions coordinate to produce behavior requires models that are both predictive and interpretable. We introduce Behavior-Adaptive Connectivity Estimation (BACE), an end-to-end framewor…
Bond-Centered Molecular Fingerprint Derivatives: A BBBP Dataset Study
Bond Centered FingerPrint (BCFP) are a complementary, bond-centric alternative to Extended-Connectivity Fingerprints (ECFP). We introduce a static BCFP that mirrors the bond-convolution used by directed message-passing G…
Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series
Recently, methods that represent data as a graph, such as graph neural networks (GNNs) have been successfully used to learn data representations and structures to solve classification and link prediction problems. The ap…
Connectivity EstimationLink PredictionTime SeriesTime Series AnalysisComputing and Learning on Combinatorial Data
The twenty-first century is a data-driven era where human activities and behavior, physical phenomena, scientific discoveries, technology advancements, and almost everything that happens in the world resulting in massive…