Springer Estimating Functional Connectivity and Topology Book

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.