42-101: Introduction to Biomedical Engineering (12)
This course will provide exposure to basic biology and engineering problems associated with living systems and health care delivery. Examples will be used to illustrate how basic concepts and tools of science & engineering can be brought to bear in understanding, mimicking and utilizing biological processes. The course will focus on four areas: biotechnology, biomechanics, biomaterials and tissue engineering and bioimaging and will introduce the basic life sciences and engineering concepts associated with these topics. Pre-requisite or co-requisite: 03-121 Modern Biology.
86-631/42-631: Neural Data Analysis (12)
The vast majority of behaviorally relevant information is transmitted through the brain by neurons as trains of actions potentials. How can we understand the information being transmitted? This class will cover the basic engineering and statistical tools in common use for analyzing neural spike train data, with an emphasis on hands-on application. Topics will include neural spike train statistics, estimation theory (MLE, MAP), signal detection theory (d-prime, ROC analysis), information theory (entropy, mutual information, neural coding theories, spike-distance metrics), discrete classification (naive Bayes), continuous decoding (PVA, OLE, Kalman), and white-noise analysis. Each topic covered will be linked back to the central ideas from undergraduate probability, and each assignment will involve actual analysis of neural data, either real or simulated, using Matlab. This class is meant for upper-level undergrads or beginning graduate students, and is geared to the engineer who wants to learn the neurophysiologist's toolbox and the neurophysiologist who wants to learn new tools.
86-601: Topics in Motor Control (3)
This course will delve into the literature on the neural control of movement, to gain a deep understanding of how movements are planned, coordinated, and executed. Our goal will be to synthesize the major research findings, by sifting out and summarizing the data that support various theories of motor control. Topics to be covered include representation (muscles vs. movements, reference frames), the role of feedback circuitry (basal ganglia, cerebellum), and computational frameworks (internal models, optimal control).
