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Springer Estimating Functional Connectivity in Neural Assemblies - Springer Theses

Amazon

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Description

This comprehensive research monograph presents innovative statistical algorithms for inferring functional connectivity within large-scale neural assemblies. The methods prioritise computational accuracy and efficiency whilst faithfully reconstructing both inhibitory and excitatory functional links across complex neuronal networks. The book details statistical approaches for identifying the most significant functional connectivity graphs and demonstrates how graph theory can extract topological network features. Designed for researchers working in computational neuroscience and systems neurology, this Springer Thesis combines rigorous mathematical foundations with practical algorithmic implementations to advance understanding of neural assembly organisation and dynamics.

Key Features

Statistical algorithms infer functional connectivity patterns in large-scale neural assemblies with maximised computational accuracy.

Methods reconstruct both inhibitory and excitatory functional links within neuronal networks.

Graph theory techniques extract topological features from computed functional connectivity networks.

Springer publication provides peer-reviewed research for neuroscience and computational biology professionals.

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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