國立虎尾科技大學 |

Estimating Functional Connectivity and Topology in Large-Scale Neuronal Assemblies = Statistical and Computational Methods /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Estimating Functional Connectivity and Topology in Large-Scale Neuronal Assemblies/ by Vito Paolo Pastore.
其他題名:	Statistical and Computational Methods /
作者:	Pastore, Vito Paolo.
面頁冊數:	XV, 87 p. 43 illus., 39 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Graph Theory. -
電子資源:	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. - 1st ed. 2021. - XV, 87 p. 43 illus., 39 illus. in color.online resource. - Springer Theses, Recognizing Outstanding Ph.D. Research,2190-5061. - Springer Theses, Recognizing Outstanding Ph.D. Research,.

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:

786670
Graph Theory.

LC Class. No.: R856-857

Dewey Class. No.: 610.28

Estimating Functional Connectivity and Topology in Large-Scale Neuronal Assemblies = Statistical and Computational Methods /
LDR:02790nam a22004335i 4500 001 1047942
003 DE-He213
005 20211014195855.0
007 cr nn 008mamaa
008 220103s2021 sz | s |||| 0|eng d
020 $a 9783030590420 $9 978-3-030-59042-0
024 7 $a 10.1007/978-3-030-59042-0 $2 doi
035 $a 978-3-030-59042-0
050 4 $a R856-857
050 4 $a HC79.E5
050 4 $a GE220
072 7 $a MQW $2 bicssc
072 7 $a TEC059000 $2 bisacsh
072 7 $a MQW $2 thema
082 0 4 $a 610.28 $2 23
100 1 $a Pastore, Vito Paolo. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1351694
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.
250 $a 1st ed. 2021.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a XV, 87 p. 43 illus., 39 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Springer Theses, Recognizing Outstanding Ph.D. Research, $x 2190-5061
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 2 4 $a Graph Theory. $3 786670
650 2 4 $a Coding and Information Theory. $3 669784
650 2 4 $a Complexity. $3 669595
650 1 4 $a Biomedical Engineering and Bioengineering. $3 1211019
650 0 $a Graph theory. $3 527884
650 0 $a Information theory. $3 595305
650 0 $a Coding theory. $3 561460
650 0 $a Computational complexity. $3 527777
650 0 $a Biomedical engineering. $3 588770
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030590413
776 0 8 $i Printed edition: $z 9783030590437
776 0 8 $i Printed edition: $z 9783030590444
830 0 $a Springer Theses, Recognizing Outstanding Ph.D. Research, $x 2190-5053 $3 1253569
856 4 0 $u https://doi.org/10.1007/978-3-030-59042-0
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)