£113.48

Springer Estimating Functional Connectivity and Topology Book

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Description

This academic text from Springer provides a deep look into novel statistical algorithms for inferring the functional connectivity of large-scale neural assemblies. Designed for researchers and scientists, the methods focus on maximizing computational accuracy and efficiency while maintaining a faithful reconstruction of both inhibitory and excitatory functional links. Readers will find detailed reports on statistical methods used to compute the most significant functional connectivity graph. The book also explains how to apply graph theory to extract the topological features of these computed networks. It serves as a technical resource for understanding the mathematical and computational frameworks necessary to study complex neuronal systems and their underlying network structures.

Key Features

Provides novel statistical algorithms specifically designed to infer functional connectivity within large-scale neural assemblies.

Focuses on maximizing computational accuracy and efficiency to handle complex neural data effectively.

Reconstructs both inhibitory and excitatory functional links to provide a complete view of neural connections.

Includes statistical methods for computing the most significant functional connectivity graphs.

Demonstrates how to use graph theory to extract specific topological features from computed networks.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
07 November 2021
Listed Since
11 October 2021

Barcode

No barcode data available

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