Connectivity (graph theory)

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Related lectures (30)

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Networked Control Systems: Graph Theory and Stochastic Matrices

Explores graph theory, stochastic matrices, consensus algorithms, and spectral properties in networked control systems.

Neural Signals and Connectomes

Explores neural signals, connectomes, graph theory, and multi-voxel pattern analysis in fMRI trials.

Spectral Clustering: Theory and Applications

Explores spectral clustering theory, eigenvalue decomposition, Laplacian matrix, and practical applications in identifying clusters.

Graph Theory Fundamentals

Covers the fundamentals of graph theory, including vertices, edges, degrees, walks, connected graphs, cycles, and trees, with a focus on the number of edges in a tree.

Networks: Trees

Explains the concept of trees in graph theory and the definition of a spanning tree.

Graph Theory Basics

Introduces induced flows, basis matrices, and tree solutions in graph theory.

Networked Control Systems: Properties of Laplacian Matrices

Explores Laplacian matrix properties in networked control systems and their relation to graph theory.

Networked Control Systems: Laplacian Flow and Heat Equation

Explores Laplacian flow, heat equation analogies, and microgrid networked controllers.

Graph Theory in Neural Signal Processing

Explores neural signal processing through graph theory, multivariate methods, and fMRI analysis.

Neuronal Connectivity Patterns

Explores neuronal connectivity patterns, connection probabilities, and experimental techniques used to study synaptic connectivity.