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Owner/Developer: EDX, Harvard University and Massachusetts institute of technology
United States of America
01 January 2012
United States of America
Learn how to digitally reconstruct a single neuron to better study the biological mechanisms of brain function, behaviour and disease.
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!
Mooc (massive open online courses)
Knowledge of ordinary differential equations, and their numerical solution
Knowledge of programming in one of Python (preferred), C/C++, Java, MATLAB, R
Students, Researchers, Regulators and policy-makers, Teachers and educators, Technicians, Managers, Scientific officers / Project managers, Professionals (e.g. veterinarians), General public
Academia, Industry, Governmental bodies, Contract Research Organizations (CROs), Consulting, SMEs
Continuing Professional Development
Full coverage (a dedicated course)
|Details on the topic or technology covered:||
Week 1: Simulation neuroscience: An introduction,
Understanding the brain
Approaches and Rationale of Simulation Neuroscience
The principles of simulation neuroscience
Reconstruction and simulation strategies
Summary and Caveats Experimental data
Single neuron data collection techniques
Caveats and summary of experimental data techniques Single neuron data
Summary and Caveats
Week 2: Neuroinformatics
Introduction to neuroinformatics
Data integration and 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
Design an entity
An entity design and the provenance model
Morphological feature extraction
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
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
Week 4: Modeling synapses
Modeling synaptic potential
Modeling the potential
Rall’s cable model
Modeling synaptic transmission between neurons
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
Accessing the NMC portal
Running models on your local computer
Downloading and interacting with the single cell models
Injecting a current
What you'll learn
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
EPFL Doctoral students may get credits for this course, see EPFL Doctoral School Pages. You should apply to your program director.
Certificate available against a fee
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