The human brain is a highly sophisticated biological information processor exhibiting complex dynamics at several spatial and temporal scales. It is not surprising therefore that understanding the brain requires the development of theoretical constructs and mathematical models to interpret and predict empirical findings. In this course, you will learn about such models at the micro-, meso-, and macrolevel, how they capture essential features of neural dynamics, how simulating them can lead to new insights, and how they relate to empirical data. You will get an overview of computational neuroscience, from neuron models to models of the whole brain. During skill training sessions you will be able to consolidate your understanding of core concepts through computer exercises. Finally, you will be able to apply knowledge in a direct hands-on manner by designing and conducting your own computer simulation study. For the skills, you will have weekly peer group meetings to advance your research project (on which you will have to write a final report) by discussing your plans and your progress with your peers and the instructors. During these sessions, you will have to give short presentations wherein you need to review the relevant literature behind your research question, propose a project, update on your progress, and provide an overview of your findings.
The learning objectives of this course are: • To know and understand theoretical/mathematical models of neural processes at the microlevel (cellular), mesolevel (local circuits, neural populations), and macrolevel (large-scale brain networks, network integration). • To be able to simulate these models using tools and programming languages such as o NEURON o NEST o MATLAB o Python • To know and understand how these models are evaluated against as well as informed by empirical data. • To know and understand how these models are used as an investigative tool for neuroscientific themes discussed in the course • To be able to evaluate the possibilities & limitations of the different models in the context of a specific research question. • To be able to design and execute a simulation study addressing a neuroscientific research question. • To be able to apply their knowledge to review the scientific quality of neuroscientific papers.
The literature of this course will largely comprise of scientific articles. For a review of fundamental concepts we recommend the book: Gerstner, W., & Kistler, W. M. (2002). Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press.