Simulation Neuroscience

30 h
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  • 6 Sequences
  • Advanced Level

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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
Reconstruction and simulation strategies
Summary and Caveats

Experimental data
Single neuron data collection techniques
Morphological profiles
Electrophysiological profiles
Caveats and summary of experimental data techniques

Single neuron data
Ion channels
Combining profiles
Cell densities
Summary and Caveats
Synaptic dynamics

Week 2: Neuroinformatics
Introduction to neuroinformatics
Text mining
Data integration and knowledge graphs
Knowledge graphs
Brain atlases and knowledge space
Motivation of data-integration
Fixed data approach to data integration
Blue Brain Nexus
Architecture of Blue Brain Nexus
Design a provenance entity
Creating your own domain
Acquisition of neuron electrophysiology and morphology data
Generating data
Using data
Design an entity
An entity design and the provenance model
Morphological feature extraction
Morphological structures,
Understanding neuronal morphologies using NeuroM
Statistics and visualisation of morphometric data

Week 3: Modeling neurons
Introduction to the single neuron
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 cells

Week 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 modfile

Week 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 components

Week 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


Knowledge of ordinary differential equations, and their numerical solution

Knowledge of programming in one of Python (preferred), C/C++, Java, MATLAB, R.


Henry Markram
École polytechnique fédérale de Lausanne

Idan Segev
École polytechnique fédérale de Lausanne

Sean Hill
École polytechnique fédérale de Lausanne

Felix Schürmann
Adjunct Professor

Eilif Muller
Section Manager of Cells & Circuits in the Simulation Neuroscience Division
École polytechnique fédérale de Lausanne

Srikanth Ramaswamy
Senior Scientist in the Cells & Circuits Section of the Simulation Neuroscience Division
École polytechnique fédérale de Lausanne

Werner Van Geit
Systems Specialist

Samuel Kerrien
Section Manager, Neuroinformatics Software Engineering
École polytechnique fédérale de Lausanne

Lida Kanari
PhD student, Molecular Systems, Simulation Neuroscience Division
École polytechnique fédérale de Lausanne

Anne-Kristin Kaufmann


The École polytechnique fédérale de Lausanne (EPFL, English: Swiss Federal Institute of Technology in Lausanne) is a research university in Lausanne, Switzerland, that specialises in physical sciences and engineering.

One of the two Swiss Federal Institutes of Technology, the school was founded by the Swiss Federal Government with the stated mission to:

Educate engineers and scientists to the highest international standing
Be a national center of excellence in science and technology
Provide a hub for interaction between the scientific community and the industry
EPFL is considered one of the most prestigious universities in the world for engineering and sciences, ranking 17th overall and 10th in engineering in the 2015 QS World University Rankings; 34th overall and 12th in engineering in the 2015 Times Higher Education World University Rankings.


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