I teach the following courses at Ohio Wesleyan:
Principles of Physics I & II (PHYS 115 & 116): This is the introductory physics sequence for non-majors, taken by mostly biology students. I emphasize how an understanding of physics can shed light on problems in biology, and I incorporate many topics developed in the Introductory Physics for the Life Sciences literature (such as scaling laws and diffusion). I love seeing the light go on in students' eyes when physics helps them see the world in a new way.
Biophysics of the Brain (PHYS 300.2): This is an upper-level course that starts by exploring the biophysics of action potential generation in the individual neuron, starting with ion channels and building up to the Hodgkin-Huxley model. We then transition to mathematical modeling of the dynamics of neural circuits, as well as information processing in neural circuits. Students finish the course with an individual project in which they reproduce and extend the results of a published paper in computational neuroscience.
Digital Signal Processing (DSP) (PHYS 300.3): From driving a car to talking on the phone to watching TV, a host of modern technologies involve sophisticated mathematical analysis of digital signals. Many data sets in physics and neuroscience also require analysis using DSP techniques, such as de-noising the signals detected by LIGO which verified the existence of gravitational waves, or filtering EEG signals for use by a brain-machine interface. In this class we explore the techniques that are foundational to processing such signals. Topics include linear time-invariant systems, convolution, transfer functions, correlation, the discrete Fourier transformation, digital filtering, image processing, and time-frequency analysis. Students finish the course by completing a project in which they apply DSP tools to analyze a dataset of their choice.
Digital Electronics (PHYS 375): In this course students learn how a computer works from transistors on up. They do so by building an entire computer on a breadboard, following the second half of Horowitz and Hill's Learning the Art of Electronics. Students finish the course by completing a final project in which they program their computer to perform a specific task (such as tuning a guitar or sounding an alarm when a switch is triggered).
Computational Neuroscience (NEUR 323): This course introduces neuroscience majors to the field of computational neuroscience. It assumes no previous experience with computer programming, and it does not require calculus as a pre-requisite. In lecture, students learn fundamental principles of how neural circuits process information, while in lab they learn to apply data analysis techniques to neural data. Throughout the course students gain facility using MATLAB to write simulations and analyze data.