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dc.contributor.authorWu, Xiaomin
dc.contributor.authorBhattacharyya, Shuvra S
dc.contributor.authorChen, Rong
dc.date.accessioned2021-02-17T21:44:52Z
dc.date.available2021-02-17T21:44:52Z
dc.date.issued2021-01-06
dc.identifier.urihttp://hdl.handle.net/10713/14705
dc.description.abstractFunctional 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.en_US
dc.description.urihttps://doi.org/10.3389/fncom.2020.603765en_US
dc.language.isoenen_US
dc.publisherFrontiers Media S.A.en_US
dc.relation.ispartofFrontiers in Computational Neuroscienceen_US
dc.rightsCopyright © 2021 Wu, Bhattacharyya and Chen.en_US
dc.subjectcalcium imagingen_US
dc.subjectgraph embeddingen_US
dc.subjectmachine learningen_US
dc.subjectmicrocircuit analysisen_US
dc.subjectneural decodingen_US
dc.titleWGEVIA: A Graph Level Embedding Method for Microcircuit Data.en_US
dc.typeArticleen_US
dc.identifier.doi10.3389/fncom.2020.603765
dc.identifier.pmid33488374
dc.source.volume14
dc.source.beginpage603765
dc.source.endpage
dc.source.countrySwitzerland


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