WGEVIA: A Graph Level Embedding Method for Microcircuit Data.

ABSTRACT: Functional microcircuits are useful for studying interactions among neural dynamics of neighboring neurons during cognition and emotion. A functional microcircuit is a group of neurons that are spatially close, and that exhibit synchronized neural activities. For computational analysis, functional microcircuits are represented by graphs, which pose special challenges when applied as input to machine learning algorithms. Graph embedding, which involves the conversion of graph data into low dimensional vector spaces, is a general method for addressing these challenges. In this paper, we discuss limitations of conventional graph embedding methods that make them ill-suited to the study of functional microcircuits. We then develop a novel graph embedding framework, called Weighted Graph Embeddi

SUBMITTER: Wu X 

PROVIDER: S-EPMC7815934 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature