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Estimating Functional Connectivity and Topology in Large-Scale Neuronal Assemblies

Estimating Functional Connectivity and Topology in Large-Scale Neuronal Assemblies

Vito Paolo Pastore

209,41 €
IVA incluido
Disponible
Editorial:
Springer Nature B.V.
Año de edición:
2021
Materia
Ingeniería: general
ISBN:
9783030590444
209,41 €
IVA incluido
Disponible

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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.  

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Otros libros del autor

  • Estimating Functional Connectivity and Topology in Large-Scale Neuronal Assemblies
    Vito Paolo Pastore
    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 signif...