Estimating Functional Connectivity and Topology in Large-Scale Neuronal Assemblies

Statistical and Computational Methods

verfasst von: Dr. Vito Paolo Pastore

Verlag: Springer International Publishing

Buchreihe : Springer Theses

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

Einloggen, um Zugang zu erhalten

Über dieses Buch

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.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract

Reconstruction of the human connectome, as the set of structural and functional brain’s neuronal interconnections at different scales, is a fundamental issue in modern neuroscience. Adopting reduced and simplified models may represent an efficient strategy to overcome the complexity of the brain’s neural circuits. This manuscript reports on statistical algorithms designed to infer functional connectivity of in vitro neural networks chronically coupled to Micro Electrodes Arrays (MEAs). The developed collection includes algorithms designed to maximize computational accuracy (e.g., successfully reconstructing the inhibitory functional links) and efficiency. This PhD thesis comprises methods to compute the most significant functional connectivity graph, while extracting its topological properties based on graph theory.

Vito Paolo Pastore

Chapter 2. Materials and Methods

Abstract

Cross-correlation [1] measures the frequency at which one particular neuron or electrode fires (“target”) as a function of time.

Vito Paolo Pastore

Chapter 3. Results

Abstract

In the first section of the Results, I will consider each of the connectivity methods that I developed and implemented during my Ph.D.

Vito Paolo Pastore

Backmatter

Titel: Estimating Functional Connectivity and Topology in Large-Scale Neuronal Assemblies
verfasst von: Dr. Vito Paolo Pastore
Verlag: Springer International Publishing
Electronic ISBN: 978-3-030-59042-0
Print ISBN: 978-3-030-59041-3
DOI: https://doi.org/10.1007/978-3-030-59042-0