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Simulation Neuroscience

13,640 people completed this program


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!

  • 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


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

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Simulation Neuroscience
6 weeks
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