Math 568: Computational and Mathematical Neuroscience
In the field of neuroscience, the brain is investigated at many different levels, from the activity of single neurons, to computations in small local networks, to the dynamics of large neuronal populations. This course introduces students to modeling and quantitative techniques used to investigate, analyze and understand the brain at these different levels.
Neurosci 613: Neurophysiology and Computational Neuroscience
This 1-credit module of Neurosci 601 focuses on neurophysiology and provides an introduction to computational modeling of neurons and neural networks for graduate students in neuroscience and biological sciences.
Math 563: Advanced Mathematical Methods for the Biological Sciences
This course focuses on mathematical modeling of how biological objects, such as molecules, cells or whole organisms, move and interact in time and in space. Depending on the biological process, time scales for interaction can vary widely, from milliseconds to days, and spatial scales can vary from microns to miles, however we can use similar mathematical techniques to capture behavior of these diverse systems. Classical techniques stem from the theory of random walks and involve partial differential equations (PDEs). More recent techniques include agent-based modeling that can simulate highly complex and variable interactions among many biological objects.