Victoria Booth


Fall 2013

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.

Winter 2013

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.

Fall 2011

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.

Winter 2008

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.