Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Lecture

Scientific Computing in Neuroscience

In course

NX-450: Computational neurosciences: biophysics

The course introduces students to a synthesis of modern neuroscience and state-of-the-art data management, modelling and computing technologies with a focus on the biophysical level. ...

Description

This lecture covers the fundamentals of scientific computing in neuroscience, focusing on the simulation of neurons and networks using tools like NEURON, NEST, and BRIAN. It discusses the history of computational science, the scaling of computing systems, and the challenges of computing at the forefront of technology. The lecture also delves into the modeling of neurons at different scales, the understanding of neural circuits, and the importance of biophysical and biochemical computational modeling in studying brain phenomena.

This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.

Watch on Mediaspace

Instructors (2)

Armando Romani

Felix Schürmann

Felix Schürmann is co-director of the Blue Brain Project and involved in several research challenges of the European Human Brain Project. He studied physics at the University of Heidelberg, Germany, supported by the German National Academic Foundation. Later, as a Fulbright Scholar, he obtained his Master's degree (M.S.) in Physics from the State University of New York, Buffalo, USA, under the supervision of Richard Gonsalves. During these studies, he became curious about the role of different computing substrates and dedicated his master thesis to the simulation of quantum computing. He studied for his Ph.D. at the University of Heidelberg, Germany, under the supervision of Karlheinz Meier. For his thesis he co-designed an efficient implementation of a neural network in hardware.

Official source

Ontological neighbourhood

Biology

Cell biology: Neurotransmission

Mathematics

Analysis: Numerical analysis

Related concepts (32)

Computational science

Computational science, also known as scientific computing, technical computing or scientific computation (SC), is a division of science that uses advanced computing capabilities to understand and solve complex physical problems. This includes Algorithms (numerical and non-numerical): mathematical models, computational models, and computer simulations developed to solve sciences (e.

Numerical methods for ordinary differential equations

Numerical methods for ordinary differential equations are methods used to find numerical approximations to the solutions of ordinary differential equations (ODEs). Their use is also known as "numerical integration", although this term can also refer to the computation of integrals. Many differential equations cannot be solved exactly. For practical purposes, however – such as in engineering – a numeric approximation to the solution is often sufficient. The algorithms studied here can be used to compute such an approximation.

Artificial neuron

An artificial neuron is a mathematical function conceived as a model of biological neurons, a neural network. Artificial neurons are elementary units in an artificial neural network. The artificial neuron receives one or more inputs (representing excitatory postsynaptic potentials and inhibitory postsynaptic potentials at neural dendrites) and sums them to produce an output (or , representing a neuron's action potential which is transmitted along its axon).

Verlet integration

Verlet integration (vɛʁˈlɛ) is a numerical method used to integrate Newton's equations of motion. It is frequently used to calculate trajectories of particles in molecular dynamics simulations and computer graphics. The algorithm was first used in 1791 by Jean Baptiste Delambre and has been rediscovered many times since then, most recently by Loup Verlet in the 1960s for use in molecular dynamics. It was also used by P. H. Cowell and A. C. C.

Computational physics

Computational physics is the study and implementation of numerical analysis to solve problems in physics. Historically, computational physics was the first application of modern computers in science, and is now a subset of computational science. It is sometimes regarded as a subdiscipline (or offshoot) of theoretical physics, but others consider it an intermediate branch between theoretical and experimental physics - an area of study which supplements both theory and experiment.

Related lectures (38)

Scientific Computing in Neuroscience

Explores the history and tools of scientific computing in neuroscience, emphasizing the simulation of neurons and networks.

Biological Scope and Computational Simulation

Explores connecting biological scope with computational simulation methods and discusses various simulation environments for modeling neural activity.

Neural Signals and Signal Processing: Modeling and Simulation

Covers the fundamentals of neural signals and signal processing, focusing on modeling and simulation of neural systems.

Modeling the electrical activity of the neuron MOOC: Simulation Neurocience

Explores the modeling of neuron electrical activity, including ion channels and concentrations, Nernst equation, and resting potential.

Engineering Neurons: Optogenetics