Lecture

Introduction to Networks and Brain Networks

Related lectures (32)

Covers the basics of networks, focusing on brain networks, historical breakthroughs, small-world and scale-free network discoveries, and the importance of the human connectome.

Fundamentals of Brain Connectomics

Introduces the basics of brain connectomics, including terminology, data preprocessing, functional MRI, connectivity measures, and modular structure.

Brain Connectomics: Basics of Neuroimaging

Covers the basics of brain connectomics, including brain networks, terminology, data schemes, preprocessing, node connectivity, and functional connectome structure.

Graph Models and Brain Connectomics

Explores graph theory in brain connectomics, MRI applications, network analysis relevance, and individual fingerprinting.

Neural Signals and Connectomes

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

Handling Networks: Graph Theory

Covers the fundamentals of handling networks and centrality measures in graph theory.

Topology in Complex Networks: Insights from Topological Data Analysis

Explores the role of higher-order topological properties in complex networks using topological data analysis for structural break and price anomaly detection.

Connectivity-Informed Brain Activity Interpretation

Explores integrating brain connectivity to decode and interpret brain activity using graph signal processing and spectral residual networks.

Statistical analysis of network data

Covers stochastic properties, network structures, models, statistics, centrality measures, and sampling methods in network data analysis.

Graph Theory in Neural Signal Processing

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

Networks, Flows

Covers the definition of flow in a network and flow calculation.

Structured Synaptic Connectivity

Explores the structured synaptic connectivity between hippocampal regions using techniques like mGRASP and modeling.

Handling Networks: Graph Theory

Explores graph theory concepts, centrality measures, and real-world network properties, providing insights into handling diverse types of networks.

Fixed Points in Graph Theory

Focuses on fixed points in graph theory and their implications in algorithms and analysis.

Data-Driven Modeling in Neuroscience: Meenakshi Khosla

By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.

Node Degree and Strength

Explores brain node connectivity, node degree, strength, random networks, power law distributions, and the complexity of real networks.

Centrality and Hubs

Explores centrality, hubs, eigenvectors, clustering coefficients, small-world networks, network failures, and percolation theory in brain networks.

Non-parametric regression for networks

Explores non-parametric regression for networks, covering object data analysis, network graphs, extrinsic distances, and practical projections.

Neuronal Connectivity Patterns

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

Graph Theory and Network Flows

Introduces graph theory, network flows, and flow conservation laws with practical examples and theorems.