Computational neuroscience aims to design and utilize quantitative methods in neurophysiology and neurobiology. The development of these methods has significantly enhanced our understanding of the functioning of the nervous system and is increasingly becoming vital for the neuroscience community. Expanding training in the application of computational methods to neuroscience problems is a widely recognized need.
Students enrolled in this curriculum will learn to develop mathematical models and computer simulations for various neuroscientific objectives. The models will be used (1) for basic science: to better understand the development and functions of the nervous system in different animal models, and (2) for translational science: to develop wearable and implantable medical devices (“neurotechnologies”) to restore and repair compromised nervous systems due to neurological disorders or traumatic injuries. This curriculum will provide cross-cutting skills that can also be applied in other curricula of the doctoral school. In particular, the models will be based on biophysics, machine learning, advanced signal processing, and will address the neuroscientific problem at different levels. Students will also be exposed to empirical techniques in neuroscience and neurophysiology to strengthen their skills and enable them to experimentally validate their models.