Simulation Neuroscience
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Owner/Developer: EDX, Harvard University and Massachusetts institute of technology
| Country: |
United States of America |
|---|---|
| Languages: |
English |
| Url: |
https://www.edx.org/course/simulation-neuroscience-epflx-simneurox |
| Created: |
01 January 2012 |
| Locations: |
United States of America |
| Description: | 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! |
| Format: |
Mooc (massive open online courses) |
| Presence: |
Mandatory |
| Access: |
Free |
| Content type: |
Theoretical, Practical |
| Duration: |
6 weeks |
| Prerequisites: |
Knowledge of ordinary differential equations, and their numerical solution Knowledge of programming in one of Python (preferred), C/C++, Java, MATLAB, R |
| Target audience: |
Students, Researchers, Regulators and policy-makers, Teachers and educators, Technicians, Managers, Scientific officers / Project managers, Professionals (e.g. veterinarians), General public |
| Target sectors: |
Academia, Industry, Governmental bodies, Contract Research Organizations (CROs), Consulting, SMEs |
| Educational level: |
Continuing Professional Development |
| 3rs relevance: |
Replacement |
| Topics covered: |
Computational methods |
| 3rs coverage: |
Full coverage (a dedicated course) |
| Details on the topic or technology covered: |
Course Syllabus 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 |
| Learning outcome: |
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 |
| Qualification received: |
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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