Estimating functional connectivity and topology in large-scale neuronal assemblies : statistical and computational methods / Vito Paolo Pastore.
2021
QP363.3
Linked e-resources
Linked Resource
Online Access
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks
Details
Title
Estimating functional connectivity and topology in large-scale neuronal assemblies : statistical and computational methods / Vito Paolo Pastore.
Author
Pastore, Vito Paolo, author.
ISBN
9783030590420 (electronic book)
3030590429 (electronic book)
3030590410
9783030590413
3030590429 (electronic book)
3030590410
9783030590413
Published
Cham, Switzerland : Springer, [2021]
Language
English
Description
1 online resource (XV, 87 pages 43 illustrations, 39 illustrations in color.)
Item Number
10.1007/978-3-030-59042-0 doi
Call Number
QP363.3
Dewey Decimal Classification
612.8/2
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.
Note
"Doctoral thesis accepted by the University of Genova, Italy."
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed January 29, 2021).
Series
Springer theses, 2190-5053
Available in Other Form
Print version: 9783030590413
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
All Resources
All Resources
Table of Contents
Introduction
Materials and Methods
Results
Conclusion.
Materials and Methods
Results
Conclusion.