Cellular Neurophysiology - The nervous system from a functional perspective. The goal is to understand how ion channels and other components of nerve cells give rise to electrical excitability and synaptic function, and how those properties are then used for coding information and higher order function in the nervous system.

Systems Neuroscience - Neurophysical phenomena from a systems perspective. Basic concepts of cellular neuroscience, such as excitability, impulse conduction, and integration of activity at the cellular, before focusing on network level physiology of the nervous system and its role in the generation of behavior. The basic knowledge to understand neurobiological processes at all levels of complexity.

Biological Imaging Techniques - A variety of approaches to examine biological structures at different microscopic scales: conventional light microscopy, fluorescent microscopy, modern high resolution light microscopy, and electron microscopy. Optical approaches to study the dynamics of cellular function, including calcium and voltage imaging, and molecular interactions.

Neural Engineering - Understanding how the brain functions using engineering principles. Different instrumentation and signal processing algorithms to study how the brain functions, how to detect different pathologies and new applications for research. Basic overview of neurology, vector populations, neural networks, vision research, functional MRI, functional electrical stimulation, neural prosthetics, and other advanced research topics studying neurology.

Medical Imaging Systems - Detailed introduction to medical imaging physics, instrumentation, data acquisition and image processing systems for reconstruction of multi-dimensional anatomical and functional medical images. Three-Dimensional medical imaging modalities including X-ray, Computer Tomography, Magnetic Resonance Imaging, Single Photon Emission Computer Tomography, Positron Emission Tomography, Ultrasound and optical imaging modalities are included.

Approaches to Quantitative Analysis in the Life Sciences - Case studies of common data analytic methods used in the life sciences. The case studies are designed to help students who are interested in applications of statistical thinking to biological sciences appreciate the scope of quantitative methods, their underlying concepts, assumptions and limitations. While the mathematics of specific methods are not covered, students of the course will get and understanding of the diverse approaches to statistical inference in the life sciences.

Intro to Comp Neuroscience - The modeling, computational and analysis techniques for single neurons and small neuronal networks. Knowledge of neurobiology, electric circuits and numerical tools for the solution of differential equations.

Advanced Comp Neuroscience - Modeling and computational analysis of biological neuronal networks.

Computational Systems Biology - Introduction to the mathematical and computational modeling of biological systems with a focus on chemical, biochemical, metabolic and genetic networks. Knowledge of biology and numerical tools for the solution of differential equations.