Estimating Functional Connectivity and Topology in Large-Scale Neuronal Assemblies - Statistical and Computational Methods (Hardcover, 1st ed. 2021)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Springer Theses
Release date:	November 2020
First published:	2021
Authors:	Vito Paolo Pastore
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	87
Edition:	1st ed. 2021
ISBN-13:	978-3-03-059041-3
Categories:	Books > Reference & Interdisciplinary > Communication studies > Coding theory & cryptology Books > Science & Mathematics > Mathematics > Combinatorics & graph theory Books > Reference & Interdisciplinary > Communication studies > Information theory > Cybernetics & systems theory Books > Medicine > Nursing & ancillary services > Biomedical engineering > General
LSN:	3-03-059041-0
Barcode:	9783030590413

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