Concept

Neuroplasticity

Related courses (20)

BIO-480: Neuroscience: from molecular mechanisms to disease

The goal of the course is to guide students through the essential aspects of molecular neuroscience and neurodegenerative diseases. The student will gain the ability to dissect the molecular basis of

BIO-482: Neuroscience: cellular and circuit mechanisms

This course focuses on the biophysical mechanisms of mammalian brain function. We will describe how neurons communicate through synaptic transmission in order to process sensory information ultimately

NX-421: Neural signals and signal processing

Understanding, processing, and analysis of signals and images obtained from the central and peripheral nervous system

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.

NX-436: Advanced methods for human neuromodulation

Neuromodulation is an expending field especially in human translational neuroscience and neurotechnology. This course will introduce to different approaches / technologies for neuromodulation, their u

BIOENG-310: Neuroscience foundations for engineers

This overview course bridges computational expertise with neuroscience fundamentals, aimed at fostering interdisciplinary communication and collaboration for engineering-based neuroscience programs.

MSE-101(a): Materials:from chemistry to properties

Ce cours permet l'acquisition des notions essentielles relatives à la structure de la matière, aux équilibres et à la réactivité chimique en liaison avec les propriétés mécaniques, thermiques, électri

ME-212: Industrial production processes

Application des principales catégories de procédés de production. Modèles physiques élémentaires décrivant le comportement des principaux procédés de production. Compréhension de base des aspects éc

ME-331: Solid mechanics

Model the behavior of elastic, viscoelastic, and inelastic solids both in the infinitesimal and finite-deformation regimes.

EE-619: Advanced topics in network neuroscience

The main goal of this course is to give the student a solid introduction into approaches, methods, and tools for brain network analysis. The student will learn about principles of network science and

NX-423: Translational neuroengineering

This course integrates knowledge in basic, systems, clinical and computational neuroscience, and engineering with the goal of translating this integrated knowledge into the development of novel method

ME-414: Computational multi-scale modeling of solids

This course considers the multi-scale computational modeling of hard-matter systems, with an emphasis on the physical phenomena of matter transport and emergent macroscopic mechanical properties, and

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

BIOENG-486: Sensorimotor neuroprosthetics

Teaching objectives: history, upper limb and hand neuroprostheses, lower limb neuroprostheses, student project.

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.

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

BIO-615: Neural circuits for reward and aversion learning

Animals must learn from past experiences, to adapt their behavior to an ever-changing environment. Students will learn about the neuronal circuit mechanisms of reward-based learning, and of aversively

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

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