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Courses

BME E72 5906: Brain Networks

Large networks of interconnecting elements are now accessible for study with increasingly sophisticated simulation methods. Brain networks represent an exceptionally attractive target for such study. This course includes a survey of modern analytic methodology used to evaluate a range of biological neural networks from relatively simple cellular networks in model animals and in vitro to abstracted networks of functional areas in the human cerebral cortex. Coursework will involve lectures on methodology and recent findings as well as readings from the primary literature.

  

BME E72 533: Biomedical Signal Processing

Purpose of this course is to introduce graduate students with little signal processing background to advanced signal processing techniques that could be useful for their research. Students in the past have fit the profile of students for whom the course was designed and, but students more recently are more advanced and could benefit from advanced ESE courses. Otherwise, the practical exercises definitely give students confidence in applying signal processing techniques to their research.

 
BME E72 504: Light Microscopy and Optical Imaging 

Purpose of this survey course is to introduce students to the fundamentals of light microscopy and more advanced optical imaging modalities. Students design their own imaging experiment on paper and write a grant. Students report that learning the theory behind microscopy improves their use of microscopy for research.

 

BME E62 301A, Quantitative Physiology I

A course (lectures and supervised laboratory sections) designed to elaborate the physiological background necessary for advanced work in biomedical engineering. A quantitative model-oriented approach to physiological systems is stressed. Topics include nerve action potentials; electromyography; skeletal muscle mechanics.

 

Biol L41 5691, Mathematics and Statistics of Experimental Neuroscience, Fundamentals of Statistics Module

This module is intended to introduce a diverse group of graduate students to the theory behind descriptive and inferential statistics, statistical estimators, hypothesis testing and regression analysis. Students should be able to take what they learn and improve their use of statistics in their research.
 

Psych L33 519, Advanced Cognitive, Computational and Systems Neuroscience, Sensation and Perception Module

This module takes a single topic of sensory neuroscience and traces it to the present through selected readings. Students learn about issues of sensory coding, how relevant experiments are devised and conducted, as well as the confidence with which we can draw certain results.

 

Biol 5651, Neural Systems Lab, Auditory Module

This module is intended to give graduate students an overview of the anatomy and physiology of the auditory system.