Biology & Life Sciences Basics
Fundamental principles and concepts
EPFLx: Simulation Neuroscience
Learn how to digitally reconstruct a single neuron to better study the biological mechanisms of brain function, behaviour and disease.
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Course Insight
Designed for advanced/expert practitioners. Designed for experienced practitioners. We recommend having a solid grasp of Biology & Life Sciences fundamentals before starting this specialization.
Advanced LevelCertification IncludedSelf-Paced Learning
SKILLS TO
MASTER
Practical ApplicationTrending
Real-world project implementationBest Practices
Industry standard workflows and guidelinesCareers:Data Scientist, Data Analyst, Machine Learning Engineer.
Quick Facts
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What You’ll Learn
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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Outcomes
- 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.
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