Simulation Neuroscience: Digital Brain Reconstruction
Explore neuroscience modeling techniques to create virtual neurons and analyze neural circuits for understanding brain dynamics and pathologies.
Explore neuroscience modeling techniques to create virtual neurons and analyze neural circuits for understanding brain dynamics and pathologies.
Dive into the cutting-edge field of Simulation Neuroscience with this advanced course. Learn to integrate diverse neuroscience data into computer simulations, creating a unified empirical picture of the brain. Master the skills to digitally reconstruct biological neurons and synapses using state-of-the-art modeling tools from the Human Brain Project's Brain Simulation Platform. This course, taught by world-renowned scientists, covers data collection, annotation, and registration techniques, as well as simulation strategies and model constraining with experimental data. Ideal for those passionate about understanding the brain through computational approaches.
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What you'll learn
Analyze and categorize different types of neuroscience data for simulations
Master techniques for collecting, annotating, and registering diverse neuroscience data
Develop proficiency in simulation neuroscience strategies and neuroinformatics tools
Create detailed models of neurons, including soma, dendrites, axons, and synapses
Use experimental data on neuronal activity to constrain and validate models
Apply knowledge of synaptic dynamics in modeling neural networks
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 6 modules in this course
This course offers a comprehensive introduction to Simulation Neuroscience, an emerging approach to integrate diverse neuroscience data into computer simulations. Students will learn to create digital reconstructions of neurons and synapses, using state-of-the-art modeling tools from the Human Brain Project's Brain Simulation Platform. The curriculum covers a wide range of topics, including neuroscience data types, data collection and annotation techniques, simulation strategies, neuron classification, synaptic characteristics, and behavioral aspects. Practical skills taught include modeling neurons with all their components, using experimental data to constrain models, and working with neuroinformatics tools. The course emphasizes hands-on experience, with students learning to use platforms like Blue Brain Nexus and the NMC portal. By the end of the course, participants will have gained both theoretical knowledge and practical skills in computational neuroscience, preparing them for advanced research in brain simulation and analysis.
Simulation neuroscience: An introduction
Module 1
Neuroinformatics
Module 2
Modeling neurons
Module 3
Modeling synapses
Module 4
Constraining neurons models with experimental data
Module 5
Exam week
Module 6
Fee Structure
Instructors
3 Courses
Pioneer in Computational Neuroscience and Brain Modeling
Sean Lewis Hill is an American neuroscientist who serves as a Professor at the University of Toronto Faculty of Medicine and holds a position as Titular Professor at École polytechnique fédérale de Lausanne (EPFL). After completing his Ph.D. in computational neuroscience at the Université de Lausanne, he held postdoctoral positions at The Neurosciences Institute and the University of Wisconsin before joining IBM's T.J. Watson Research Center
Pioneer in Computational Neuroscience and Brain Research
Professor Idan Segev stands as a distinguished figure in computational neuroscience as the David & Inez Myers Professor at the Hebrew University of Jerusalem. After completing his B.Sc. in Mathematics and Ph.D. in experimental and theoretical neurobiology, he has made significant contributions to understanding brain function through his leadership of the Interdisciplinary Center for Neural Computation (ICNC). His research combines computational and theoretical approaches to study neuronal behavior and brain adaptation, with his team working internationally to model mammalian cortex function
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