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.
Understanding, processing, and analysis of signals and images obtained from the central and peripheral nervous system
The course covers the fundaments of bioelectronics and integrated microelectronics for biomedical and implantable systems. Issues and trade-offs at the circuit and systems levels of invasive microelec
Machine learning and data analysis are becoming increasingly central in sciences including physics. In this course, fundamental principles and methods of machine learning will be introduced and practi
Students will acquire an integrative view on biological and artificial algorithms for controlling autonomous behaviors. Students will synthesize and apply this knowledge in oral presentations and comp
Neural interfaces (NI) are bioelectronic systems that interface the nervous system to digital technologies. This course presents their main building blocks (transducers, instrumentation & communicatio
This course provides an overview and introduces modern methods for reinforcement learning (RL.) The course starts with the fundamentals of RL, such as Q-learning, and delves into commonly used approac
"In silico Neuroscience" introduces students to a synthesis of modern neuroscience and state-of-the-art data management, modelling and computing technologies.
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
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
