£103.96

Springer Estimating Functional Connectivity and Topology Book

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Description

Explore advanced statistical and computational methods for analyzing large-scale neuronal assemblies with this specialized Springer thesis. This book introduces a set of novel statistical algorithms specifically designed to infer functional connectivity. By focusing on maximizing computational accuracy and efficiency, these methods allow researchers to faithfully reconstruct both inhibitory and excitatory functional links within neural networks. Beyond connectivity, the text provides detailed reports on statistical methods used to compute the most significant functional connectivity graph. It also demonstrates how to apply graph theory to extract the topological features of the computed network. This resource is a valuable addition for professionals and students working in information systems and computational neuroscience who require precise tools for network reconstruction and topological analysis.

Key Features

Provides novel statistical algorithms designed to infer the functional connectivity of large-scale neural assemblies.

Focuses on maximizing computational accuracy and efficiency during the reconstruction of neural links.

Enables the faithful reconstruction of both inhibitory and excitatory functional links within the network.

Includes statistical methods to compute the most significant functional connectivity graph for research applications.

Demonstrates the use of graph theory to extract essential topological features from computed networks.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
07 November 2020
Listed Since
05 August 2020

Barcode

No barcode data available

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