Catégorie
Neurosciences computationnelles
Cours associés (30)
BIOENG-490: Project in computational neurosciences
The student will engage in a laboratory-based project in the field of computational neuroscience in one of the research labs of the EPFL working in this field.
BIOENG-450: In silico neuroscience
"In silico Neuroscience" introduces students to a synthesis of modern neuroscience and state-of-the-art data management, modelling and computing technologies.
BIOENG-448: Fundamentals of neuroengineering
Neuroengineering is at the frontier between neuroscience and engineering: understanding how the brain works allows developing engineering applications and therapies of high impact, while the design of
BIO-465: Biological modeling of neural networks
In this course we study mathematical models of neurons and neuronal networks in the context of biology and establish links to models of cognition. The focus is on brain dynamics approximated by determ
MICRO-513: Signal processing for functional brain imaging
Computational methods for the analysis of human brain imaging data
MSE-806: Spin-based devices for neuromorphic computing
The course entails a 5-days-program with lectures and exercises about spin-based computing and novel spin texture-based computing devices. An additional round table discussion and journal club session
MICRO-514: Flexible bioelectronics
The course is an introduction to the emerging field of flexible (bio)electronics. It provides an overview of the materials and processes used to design and manufacture flexible circuits and sensors.
BIO-382: Neuroscience for engineers
This optional course provides students who consider a specialization in Neuroengineering during their Master with a very broad overview of the many practical applications in the field. It should ensur
BIO-696: Neuronal circuits underlying goal-directed behavior
The brain can be viewed as a network of neurons receiving sensory input and carrying out experience- and context-dependent computations through complex synaptic interactions to drive motor output, i.e
CS-434: Unsupervised & reinforcement learning in neural networks
Learning is observable in animal and human behavior, but learning is also a topic of computer science. This course links algorithms from machine learning with biological phenomena of synaptic plastic