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Estimating functional connectivity a...

Pastore, Vito Paolo.

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Estimating functional connectivity and topology in large-scale neuronal assemblies/ by Vito Paolo Pastore.
Reminder of title:	statistical and computational methods /
Author:	Pastore, Vito Paolo.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xv, 87 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Materials and Methods -- Results -- Conclusion.
Contained By:	Springer Nature eBook
Subject:	Neural networks (Neurobiology) - Mathematical models. -
Online resource:	https://doi.org/10.1007/978-3-030-59042-0
ISBN:	9783030590420

Estimating functional connectivity and topology in large-scale neuronal assemblies = statistical and computational methods /
Pastore, Vito Paolo.

Estimating functional connectivity and topology in large-scale neuronal assembliesstatistical and computational methods /[electronic resource] :by Vito Paolo Pastore. - Cham :Springer International Publishing :2021. - xv, 87 p. :ill., digital ;24 cm. - Springer theses,2190-5053. - Springer theses..

Introduction -- Materials and Methods -- Results -- Conclusion.

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.

ISBN: 9783030590420

Standard No.: 10.1007/978-3-030-59042-0doiSubjects--Topical Terms:

891219
Neural networks (Neurobiology)
--Mathematical models.

LC Class. No.: QP363.3 / .P37 2021

Dewey Class. No.: 612.82

Estimating functional connectivity and topology in large-scale neuronal assemblies = statistical and computational methods /
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505 0 $a Introduction -- Materials and Methods -- Results -- Conclusion.
520 $a 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.
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