Neurosci 612: Networks and Computational Neuroscience
This module of NEUROSCI 601 focuses on network activity in the brain and provides an
introduction to computational modeling of neurons and neural networks. Neuroscience
faculty lectures will cover brain functions and activity that result from network interactions
in a variety of brain regions, including cortex, hippocampus and basal ganglia. Lectures
will also discuss how analyses of EEG rhythms can help understand network-dependent brain
processes such as motor control and consciousness. Computational modeling projects will
focus on understanding how networks of coupled neurons generate the dynamics underlying
these network-dependent brain processes.
Math 417: Matrix Algebra I
This course is an introduction to the properties of and operations on matrices with a
wide variety of applications. The main emphasis is on concepts and problem solving,
but students are responsible for some of the underlying theory.
Math 463: Mathematical Modeling in Biology
Math 463 provides an introduction to the use of continuous and discrete differential equations in the
biological sciences. We will develop and analyze mathematical models to investigate mechanisms underlying
specific biological processes. Another major emphasis of the course is illustrating how these models can
be used to generate predictions about currently untested conditions. The course moves from classical to
contemporary models at the population, organ, cellular, and molecular levels.
Fall 2008, Fall 2009, Fall 2010, Fall 2012
Math 559/Bioinf 800: 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.
Math 557: Methods of Applied Mathematics II: Asymptotic Analysis
Asymptotic analysis is a collection of mathematical methods used to produce
accurate approximations to solutions of equations
which cannot be solved explicitly. This course is an introduction to asymptotic
analysis with a focus on differential equations and integration.
Fall 2004, Fall 2005, Fall 2006
Math 450: Advanced Engineering Mathematics
This course covers mathematical theory and methods for the solution of
partial differential equations by separation of variables, eigenfunction
expansion and Fourier Transform. We also cover complex variable theory
including conformal mapping and complex integration.