Lectures related to Structured Synaptic Connectivity

Structured Synaptic Connectivity in Hippocampal Regions

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

In Silico Neuroscience: Network Assembly

Explores assembling in silico neuroscience networks, defining space, and dealing with sparse data to reconstruct brain regions.

Introduction to Networks and Brain Networks

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

Understanding Synaptic Transmission

Explores synaptic transmission, neurotransmitters, and neural plasticity principles.

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Neuromorphic Computing: Concepts and Hardware Implementations

Covers neuromorphic computing, challenges in ternary and binary computing, hardware simulations of the brain, and new materials for artificial brain cells.

Introduction to Circuit Connectivity MOOC: Simulating a hippocampus micro-circuit

Covers circuit connectivity in the hippocampus, including synapse distribution, divergence, and convergence.

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Scientific Computing in Neuroscience

Explores the history and tools of scientific computing in neuroscience, emphasizing the simulation of neurons and networks.

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.

Networks and Brain Networks

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.

Introduction into the module MOOC: Simulation Neurocience

Introduces nerve cells' electrical activity, focusing on spikes and synaptic potentials.

In Silico Neuroscience: Data Reproducibility and Reusability

Emphasizes data reproducibility and reusability in in silico neuroscience, focusing on neuroinformatics tools and methods.

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.

Handling Networks: Graph Theory

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

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Network Simulation and Activity Dynamics Log in to Mediaspace to watch this video

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

Neuroscience Data Analysis Log in to Mediaspace to watch this video

Explores neuroscience data analysis, focusing on K+ and Na+ currents, Ca²+ currents, and data reproducibility.

Building Neural Networks: Assembly Strategies Log in to Mediaspace to watch this video

Focuses on assembling neural network building blocks and dealing with data sparseness using various strategies and assumptions.

Network Simulation: Spontaneous Activity and Validation Log in to Mediaspace to watch this video

Covers spontaneous brain network activity, neural simulation, and validation, emphasizing the importance of in-vitro and in-vivo conditions for accurate network modeling.