國立高雄大學圖資館 |

Language: English

Back to Search results for [ author_sort:"pastore, vito paolo." ]

Estimating functional connectivity a...

Pastore, Vito Paolo.

Estimating functional connectivity and topology in large-scale neuronal assembliesstatistical and computational methods /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Estimating functional connectivity and topology in large-scale neuronal assembliesby 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.
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$q(electronic bk.)

Estimating functional connectivity and topology in large-scale neuronal assembliesstatistical 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$q(electronic bk.)

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

273715
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 assembliesstatistical and computational methods /
LDR:02344nmm a2200337 a 4500 001 595895
003 DE-He213
005 20201106125318.0
006 m d
007 cr nn 008maaau
008 211013s2021 sz s 0 eng d
020 $a 9783030590420$q(electronic bk.)
020 $a 9783030590413$q(paper)
024 7 $a 10.1007/978-3-030-59042-0 $2 doi
035 $a 978-3-030-59042-0
040 $a GP $c GP $e rda
041 0 $a eng
050 4 $a QP363.3 $b .P37 2021
072 7 $a MQW $2 bicssc
072 7 $a TEC059000 $2 bisacsh
072 7 $a MQW $2 thema
082 0 4 $a 612.82 $2 23
090 $a QP363.3 $b .P293 2021
100 1 $a Pastore, Vito Paolo. $3 888435
245 1 0 $a Estimating functional connectivity and topology in large-scale neuronal assemblies $h [electronic resource] : $b statistical and computational methods / $c by Vito Paolo Pastore.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xv, 87 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer theses, $x 2190-5053
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.
650 0 $a Neural networks (Neurobiology) $x Mathematical models. $3 273715
650 0 $a Biomedical engineering. $3 190330
650 0 $a Electrophysiology. $3 189030
650 0 $a Neural networks (Computer science) $3 181982
650 0 $a Graph theory. $3 181880
650 1 4 $a Biomedical Engineering and Bioengineering. $3 826326
650 2 4 $a Complexity. $3 274400
650 2 4 $a Coding and Information Theory. $3 273763
650 2 4 $a Graph Theory. $3 522732
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Springer theses. $3 557607
856 4 0 $u https://doi.org/10.1007/978-3-030-59042-0
950 $a Engineering (SpringerNature-11647)