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    WGEVIA: A Graph Level Embedding Method for Microcircuit Data.

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    Author
    Wu, Xiaomin
    Bhattacharyya, Shuvra S
    Chen, Rong
    Date
    2021-01-06
    Journal
    Frontiers in Computational Neuroscience
    Publisher
    Frontiers Media S.A.
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://doi.org/10.3389/fncom.2020.603765
    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 Embedding with Vertex Identity Awareness (WGEVIA), that overcomes these limitations. Additionally, we introduce a dataset, called the five vertices dataset, that helps in assessing how well graph embedding methods are suited to functional microcircuit analysis. We demonstrate the utility of WGEVIA through extensive experiments involving real and simulated microcircuit data.
    Rights/Terms
    Copyright © 2021 Wu, Bhattacharyya and Chen.
    Keyword
    calcium imaging
    graph embedding
    machine learning
    microcircuit analysis
    neural decoding
    Identifier to cite or link to this item
    http://hdl.handle.net/10713/14705
    ae974a485f413a2113503eed53cd6c53
    10.3389/fncom.2020.603765
    Scopus Count
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    UMB Open Access Articles 2021

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