Springer Estimating Functional Connectivity in Neural Assemblies - Springer Theses

This Springer Theses book presents advanced statistical algorithms designed to infer functional connectivity in large-scale neural assemblies with high computational accuracy and efficiency. The methods faithfully reconstruct both inhibitory and excitatory functional links within neuronal networks, offering researchers powerful tools for understanding complex brain organization. The book details statistical approaches to compute the most significant functional connectivity graphs and demonstrates how to apply graph theory to extract topological features from computed networks. Written for neuroscientists, computational biologists, and researchers working with large-scale neural data, this work bridges statistical methodology and practical neuronal network analysis, providing both theoretical foundations and practical applications for investigating brain connectivity patterns.