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    Causal Network Inference for Neural Ensemble Activity.

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    Author
    Chen, Rong
    Date
    2021-01-04
    Journal
    Neuroinformatics
    Publisher
    Springer Nature
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://doi.org/10.1007/s12021-020-09505-4
    Abstract
    Interactions among cellular components forming a mesoscopic scale brain network (microcircuit) display characteristic neural dynamics. Analysis of microcircuits provides a system-level understanding of the neurobiology of health and disease. Causal discovery aims to detect causal relationships among variables based on observational data. A key barrier in causal discovery is the high dimensionality of the variable space. A method called Causal Inference for Microcircuits (CAIM) is proposed to reconstruct causal networks from calcium imaging or electrophysiology time series. CAIM combines neural recording, Bayesian network modeling, and neuron clustering. Validation experiments based on simulated data and a real-world reaching task dataset demonstrated that CAIM accurately revealed causal relationships among neural clusters. © 2021, The Author(s).
    Keyword
    Causal discovery
    Clustering
    Dynamic Bayesian network
    Neuroimaging
    Identifier to cite or link to this item
    http://hdl.handle.net/10713/14354
    ae974a485f413a2113503eed53cd6c53
    10.1007/s12021-020-09505-4
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