Estimating Functional Connectivity and Topology in Large-Scale Neuronal Assemblies
Statistical and Computational Methods
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Beschreibung
This book describes a set of novel statistical algorithms designed to infer functional connectivity of large-scale neural assemblies. The algorithms are developed with the aim of maximizing computational accuracy and efficiency, while faithfully reconstructing both the inhibitory and excitatory functional links. The book reports on statistical methods to compute the most significant functional connectivity graph, and shows how to use graph theory to extract the topological features of the computed network. A particular feature is that the methods used and extended at the purpose of this work are reported in a fairly completed, yet concise manner, together with the necessary mathematical fundamentals and explanations to understand their application. Furthermore, all these methods have been embedded in the user-friendly open source software named SpiCoDyn, which is also introduced here. All in all, this book provides researchers and graduate students in bioengineering, neurophysiology and computer science, with a set of simplified and reduced models for studying functional connectivity in in silico biological neuronal networks, thus overcoming the complexity of brain circuits.
This book describes a set of novel statistical algorithms designed to infer functional connectivity of large-scale neural assemblies. The algorithms are developed with the aim of maximizing computational accuracy and efficiency, while faithfully reconstructing both the inhibitory and excitatory functional links. The book reports on statistical methods to compute the most significant functional connectivity graph, and shows how to use graph theory to extract the topological features of the computed network. A particular feature is that the methods used and extended at the purpose of this work are reported in a fairly completed, yet concise manner, together with the necessary mathematical fundamentals and explanations to understand their application. Furthermore, all these methods have been embedded in the user-friendly open source software named SpiCoDyn, which is also introduced here. All in all, this book provides researchers and graduate students in bioengineering, neurophysiology and computer science, with a set of simplified and reduced models for studying functional connectivity in in silico biological neuronal networks, thus overcoming the complexity of brain circuits.
Nominated as an outstanding Ph.D. thesis by the University of Genoa, Italy Describes statistical methods to infer functional connectivity in in vitro neuronal assemblies Explains the computation of the most significant functional connectivity graph Shows how to apply graph theory to extract topological features from the connectivity graph
Autor*in
Vito Paolo Pastore
Themen in »Estimating Functional Connectivity and Topology in Large-Scale Neuronal Assemblies«
Functional Connectivity Analysis Correlation-based Connectivity Methods Cross-correlation Histogram Graph Theory Analysis of Neuronal Assemblies In vitro Neuronal Assemblies Graph Theory Applications in Neuroscience Functional-effective Connectivity Inhibitory Connections of Multiple Neuronal Spike Trains Analysis of Neural Networks Dynamics Statistical Analysis of Spike Train Data Computational Models of Neural Connectivity Computing Functional Connectivity Matrices Emergent Neural Network Topologies Transfer Entropy in Neuroscience Delayed High Order Tranfer Entropy