Lectures in MOOC Simulation Neurocience

Synaptic transmission: Structure and Mechanisms MOOC: Simulation Neurocience

Explores the structure and function of synapses, including neurotransmitter diversity and the role of AMPA and NMDA receptors.

Modeling synaptic transmission: Stochastic Dynamics MOOC: Simulation Neurocience

Explores stochastic synaptic transmission modeling, parameters, inhibitory effects, and synaptic reliability.

Constraining neuron models with experimental data MOOC: Simulation Neurocience

Explores constraining neuron models with experimental data, emphasizing the importance of deriving parameters from experimental data.

Understanding Knowledge Graphs MOOC: Simulation Neurocience

Explores the concept of Knowledge Graphs and their role in data integration and semantic understanding, showcasing real-world examples and applications.

Modeling the synaptic potential MOOC: Simulation Neurocience

Explores the modeling of synaptic potentials and the factors influencing their amplitude.

Synapses: Structure and Dynamics MOOC: Simulation Neurocience

Explores synaptic data analysis, mapping connections, and dynamics, emphasizing the variability in synaptic responses.

Computational Aspects of Optimization MOOC: Simulation Neurocience

Explores optimization in neuron modeling, addressing underconstrained parameters, fitness functions, and successful fitting of firing patterns.

Tools for Constraining Neuron Models MOOC: Simulation Neurocience

Covers tools for constraining neuron models using eFEL and BluePyOpt.

Neuroinformatics, Text Mining MOOC: Simulation Neurocience

Explores text mining in neuroinformatics to extract brain connectivity data and annotate model parameters from scientific literature.

Hodgkin & Huxley model: Spike Dynamics MOOC: Simulation Neurocience

Explores the Hodgkin & Huxley model, focusing on spike dynamics and membrane currents in excitable cells.

Approaches and Rationale of Simulation Neuroscience MOOC: Simulation Neurocience

Explores the overview, rationale, and strategies of simulation neuroscience, emphasizing the challenges of reconstructing and simulating the brain.

Rall's Cable Model MOOC: Simulation Neurocience

Explores Rall's cable model, synaptic potential spread in dendrites, and compartmental modeling for neural simulations.

Modeling the electrical activity of the neuron MOOC: Simulation Neurocience

Explores the modeling of neuron electrical activity, including ion channels and concentrations, Nernst equation, and resting potential.

Modeling Synapse Dynamics with NMODL MOOC: Simulation Neurocience

Covers the Tsodyks-Markram model for synapse dynamics using NMODL syntax and explores vesicle release models.

Ion Channels MOOC: Simulation Neurocience

Explores the profiling and behavior of ion channels, including their diversity and classes.

Introduction to Neuroinformatics MOOC: Simulation Neurocience

Covers the heterogeneous neuroscience data, techniques like microarrays and gene sequencing, data integration, and the importance of metadata in organizing and sharing data.

Combining profiles: Electrical, Morphological, Molecular MOOC: Simulation Neurocience

Covers combining electrical, morphological, and molecular profiles to study single neurons.

Understanding Neuronal Morphologies: NeuroM Basics MOOC: Simulation Neurocience

Covers NeuroM basics, including checking neuron quality, extracting morphometrics, and visualizing neurons in different formats.

Testing your synapse model in a neuron MOOC: Simulation Neurocience

Explores testing a synapse model in a neuron using NEURON software.

Activating Synapses MOOC: Simulation Neurocience

Explains handling synapses, presynaptic cells, and multisynaptic connections in a neuron model.