About this course

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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!

At a glance

Institution: EPFLx
Subject: Biology & Life Sciences
Level: Advanced
Prerequisites:
Knowledge of ordinary differential equations, and their numerical solution

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

Language: English
Video Transcript: English

What you'll learn

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Discuss the different types of data for simulation neuroscience
How to collect, annotate and register different types of neuroscience data
Describe the simulation neuroscience strategies
Categorize different classification features of neurons
List different characteristics of synapses and behavioural aspects
Model a neuron with all its parts (soma, dendrites, axon, synaps) and its behaviour
Use experimental data on neuronal activity to constrain a model

Syllabus

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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 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
Synapses
Synapses
Synaptic dynamics

Week 2: Neuroinformatics
Introduction to neuroinformatics
Text mining
Data integration and knowledge graphs
Knowledge graphs
Ontologies
Neuroinformatics
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
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 data

Week 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 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

Learner testimonials

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"I like the content and the attempt to cover difficult and interesting material at the right level."

"I like the revision of the basic concepts of the neuronal modeling and all other nomenclatures used in neuroscience study."

"The tutorials+assignments were a good combination of exposing us to the basics of both Python and Neuron."

"Also it's great to have access to the platform (HBP Neurosimulation platform)."

About the instructors

Frequently Asked Questions

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What web browser should I use?
The Open edX platform works best with current versions of Chrome, Firefox or Safari, or with Internet Explorer version 9 and above. However,the HBP platform on which you will do your weekely exercises only works with Firefox and Chrome

What tools or programs do I need?
You will learn to use the tools of the HBP brain simulation and neuroinformatics platforms. For this, you will set up a collab at the HBP platform starting week 2.

I'm an EPFL student, can I get ECTS (credits) for this MOOC?
EPFL Doctoral students may get credits for this, see EPFL Doctoral School Pages. You should apply to your program director.

Who can take this course?

Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

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