Moreover, when providing energy to a whole district, polygeneration energy conversion technologies can take advantage of the various load profiles of the buildings by compensating the fluctuations and having therefore a smoother operation. Polygeneration energy conversion technologies indeed provide different energy services simultaneously, helping to decrease the CO2 intensity compared to energy conversion technologies that meet only one energy service. Because they meet several types of energy requirements, and for more than one single building, district energy systems represent good opportunities to implement polygeneration energy conversion technologies. District energy systems meet the heating, hot water, cooling and electricity requirements of a district. This MOOC is designed and produced by the Ecole Polytechnique Fédérale de Lausanne, in collaboration with Hebrew University, the Human Brain Projectand the Blue Brain Project and supported by the Patrick and Lina Drahi Foundation.In the present context of finding ways to decrease CO2 emissions linked with human activity, district energy systems including polygeneration energy conversion technologies are likely to play a major role. You should apply to your program director. I'm an EPFL student, can I get ECTS (credits) for this MOOC?ĮPFL Doctoral students may get credits for this, see EPFL Doctoral School Pages. For this, you will set up a collab at the HBP platform starting week 2. You will learn to use the tools of the HBP brain simulation and neuroinformatics platforms. However, the HBP platform where you will do your exercises only works with Firefox and Chrome The Open edX platform works best with current versions of Chrome, Firefox or Safari, or with Internet Explorer version 9 and above.
Lida has a MSc in Applied Mathematics and Physics and a MSc Computational Fluid Mechanics from the National Technical University of Athens, Greece. She is developing tools based on mathematical principles such as Topology, Markov Models and Stochastic Processes to model and reproduce neuronal shapes. Lida’s main focus is to understand the fundamental principles of neuronal morphologies in order to artificially reproduce them, so that they are statistically indistinguishable from the biological ones. Lida Kanari is a PhD student in the Molecular Systems Section in the Simulation Neuroscience Division. Knowledge of programming in one of Python (preferred), C/C++, Java, MATLAB, R Course Staff
Knowledge of ordinary differential equations, and their numerical solution We invite you to join us and share in our passion to reconstruct, simulate and understand the brain! Requirements Felix Schürmann and Werner Van Geit will teach you to constrain neuron models with experimental data using genetic optimization techniques
Eilif Muller you will learn the biology of synaptic transmission in the neocortex, and how to Werner van Geit will show you how to apply these methods to create your own neuron models using the NEURON simulator. Idan Segev will introduce you to the fascinating world of biological neurons, and mathematical methods to describe their biophysics. Sean Hill, Samuel Kerrien and Lida Kanari, you will learn about the structure of neuroscience data, its analysis and classification, and how to integrate it into the neuroinformatics platform. Henry Markram will open the course and present the basic principles of Simulation Neuroscience and the neuroscience knowledge it aims to integrate. In this first course, you will gain the knowledge and skills needed to create simulations of biological neurons and synapses. This is a unique, massive open online course taught by a multi-disciplinary team of world-renowned scientists.
State-of-the-art modeling tools of the HBP Brain Simulation Platform to simulate neurons, build neural networks, and perform your own simulation experiments.
In a series of three courses, you will learn to use the This is achieved by integrating diverse data sources across the various scales of experimental neuroscience, from molecular to clinical, into computer simulations The aim is to build a unified empirical picture of the brain, to 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.