Estimating functional connectivity and topology in large-scale neuronal assemblies : statistical and computational methods /

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Bibliographic Details
Author / Creator:Pastore, Vito Paolo, author.
Imprint:Cham, Switzerland : Springer, [2021]
Description:1 online resource (XV, 87 p. 43 illus., 39 illus. in color.).
Series:Springer theses : recognizing outstanding Ph.D. research, 2190-5053
Springer theses,
Subject:Neural networks (Neurobiology) -- Mathematical models.
Biomedical engineering.
Neural networks (Computer science)
Graph theory.
Biomedical engineering.
Graph theory.
Neural networks (Computer science)
Neural networks (Neurobiology) -- Mathematical models.
Electronic books.
Format: E-Resource Book
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Hidden Bibliographic Details
Notes:"Doctoral thesis accepted by the University of Genova, Italy."
Includes bibliographical references.
Online resource; title from PDF title page (SpringerLink, viewed January 29, 2021).
Summary:This book describes a set of novel statistical algorithms designed to infer functional connectivity of large-scale neural assemblies. The algorithms are developed with the aim of maximizing computational accuracy and efficiency, while faithfully reconstructing both the inhibitory and excitatory functional links. The book reports on statistical methods to compute the most significant functional connectivity graph, and shows how to use graph theory to extract the topological features of the computed network. A particular feature is that the methods used and extended at the purpose of this work are reported in a fairly completed, yet concise manner, together with the necessary mathematical fundamentals and explanations to understand their application. Furthermore, all these methods have been embedded in the user-friendly open source software named SpiCoDyn, which is also introduced here. All in all, this book provides researchers and graduate students in bioengineering, neurophysiology and computer science, with a set of simplified and reduced models for studying functional connectivity in in silico biological neuronal networks, thus overcoming the complexity of brain circuits.
Other form:Print version: 9783030590413
Standard no.:10.1007/978-3-030-59042-0