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Simulation Neuroscience is an emerging approach to integrate the knowledge dispersed throughout the field of neuroscience.
The aim is to build a unified empirical picture of the brain, to study the biological mechanisms of brain function, behaviour and disease. This is achieved by integrating diverse data sources across the various scales of experimental neuroscience, from molecular to clinical, into computer simulations.
This is a unique, massive open online course taught by a multi-disciplinary team of world-renowned scientists.In this first course, you will gain the knowledge and skills needed to create simulations of biological neurons and synapses.
This course is part of a series of three courses, where you will learn to use
state-of-the-art modeling tools of the HBP Brain Simulation Platform to simulate neurons, build neural networks, and perform your own simulation experiments.
We invite you to join us and share in our passion to reconstruct, simulate and understand the brain!
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Week 1: Simulation neuroscience: An introduction,
Understanding the brain
Approaches and Rationale of Simulation Neuroscience
The principles of simulation neuroscience
Data strategies
Neuroinformatics
Reconstruction and simulation strategies
Summary and CaveatsExperimental data
Single neuron data collection techniques
Morphological profiles
Electrophysiological profiles
Caveats and summary of experimental data techniquesSingle neuron data
Ion channels
Combining profiles
Cell densities
Summary and Caveats
Synapses
Synapses
Synaptic dynamicsWeek 2: Neuroinformatics
Introduction to neuroinformatics
Text mining
Data integration and knowledge graphs
Knowledge graphs
Ontologies
Neuroinformatics
ain atlases and knowledge space
Motivation of data-integration
Fixed data approach to data integration
lue Brain Nexus
Architecture of Blue Brain Nexus
Design a provenance entity
Ontologies
Creating your own domain
MINDS
Conclusion
Acquisition of neuron electrophysiology and morphology data
Generating data
Using data
Design an entity
An entity design and the provenance model
Conclusion
Morphological feature extraction
Morphological structures,
Understanding neuronal morphologies using NeuroM
Statistics and visualisation of morphometric dataWeek 3: Modeling neurons
Introduction to the single neuron
Introduction
Motivation for studying the electrical brain
The neuron
A structural introduction
An electrical device
Electrical neuron model
Modeling the electrical activity
Hodgkin & Huxley
Tutorial creating single cell electrical models
Single cell electrical model: passive
Making it active
Adding a dendrite
Connecting cellsWeek 4: Modeling synapses
Modeling synaptic potential
Modeling the potential
Rall's cable model
Modeling synaptic transmission between neurons
Synaptic transmission
Modeling synaptic transmission
Modeling dynamic synapses tutorial
Defining your synaps
Compiling your modifies
Hosting & testing your synaps model
Reconfigure your synaps to biological ranges
Defining a modfile for a dynamic TM synapse
Compiling and testing the modfileWeek 5: Constraining neurons models with experimental data
Constraining neuron models with experimental data
Constraining neuron model with experimental data.
Computational aspects of optimization
Tools for constraining neuron models
Tutorials for optimization
Setting up the componentsWeek 6: Exam week
NMC portal
Accessing the NMC portal
Running models on your local computer
Downloading and interacting with the single cell models
Injecting a current
