Lectures related to Neuronal Connectivity Patterns

Network Simulation and Activity Dynamics

Explores neural network simulation, activity dynamics, and validation processes to ensure accurate predictions.

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

Fundamentals of Brain Connectomics

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

Graph Theory in Neural Signal Processing

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

Structured Synaptic Connectivity

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

Neural Signals and Connectomes

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

Connectivity-Informed Brain Activity Interpretation

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

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 Theory: Girth and Independence

Covers girth, independence, probability, union bound, sets, and hypergraph recoloring.

Fixed Points in Graph Theory

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

Structured Synaptic Connectivity in Hippocampal Regions

Explores synaptic connectivity in hippocampal regions, emphasizing the complexity of neuronal networks and the role of modeling approaches.

Introduction to Networks and Brain Networks

Introduces the fundamentals of network science, focusing on brain networks and their historical breakthroughs.

Statistical analysis of network data

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

In Silico Neuroscience: Network Simulation

Delves into simulating network dynamics in in silico neuroscience, covering spontaneous and evoked activity, in-vitro and in-vivo simulations, and sensitivity analysis.

Stochastic Block Model

Covers the Stochastic Block Model and its application in community detection, exploring its mathematical formulation and challenges.

Graphical Models: Probability Distributions and Factor Graphs

Covers graphical models for probability distributions and factor graphs representation.

Probabilistic method and its applications

Introduces the probabilistic method to prove existence results in graph theory.

Graphical Models: Representing Probabilistic Distributions

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Message passing in graphical models

Explains message passing in graphical models and the matching problem in graph theory.

Networked Control Systems: Opportunities

Explores coordination in networked control systems, graph theory, and consensus algorithms.