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Estimating Functional Connectivity and Topology in Large-Scale Neuronal Assemblies

Part of the book series: Springer Theses ((Springer Theses))

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Abstract

Reconstruction of the human connectome, as the set of structural and functional brain’s neuronal interconnections at different scales, is a fundamental issue in modern neuroscience. Adopting reduced and simplified models may represent an efficient strategy to overcome the complexity of the brain’s neural circuits. This manuscript reports on statistical algorithms designed to infer functional connectivity of in vitro neural networks chronically coupled to Micro Electrodes Arrays (MEAs). The developed collection includes algorithms designed to maximize computational accuracy (e.g., successfully reconstructing the inhibitory functional links) and efficiency. This PhD thesis comprises methods to compute the most significant functional connectivity graph, while extracting its topological properties based on graph theory.

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Authors and Affiliations

  1. Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genova, Genova, Italy

    Dr. Vito Paolo Pastore

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  1. Dr. Vito Paolo Pastore
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Pastore, V.P. (2021). Introduction. In: Estimating Functional Connectivity and Topology in Large-Scale Neuronal Assemblies. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-030-59042-0_1

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  • DOI: https://doi.org/10.1007/978-3-030-59042-0_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59041-3

  • Online ISBN: 978-3-030-59042-0

  • eBook Packages: EngineeringEngineering (R0)

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