Courses required for the Ph.D. degree in Neurobiology include: Molecular and Cellular Neuroscience (NBIO 722), Systems and Transitional Neuroscience (NBIO 723), Communicating Scientific Results (NBIO 850) and Statistics for Lab Scientists (BBSP 710). Students are also required to choose and complete two electives most relevant to their thesis work. The Courses menu lists descriptions of these core courses of the Neurobiology Curriculum, as well as popular elective topics taken by students. Additional elective courses in Biochemistry, Statistics, Molecular Biology, Physiology, etc., are available to compensate specific deficiencies or enhance training. It is the current philosophy of the Curriculum faculty that students should receive a broad exposure to as many aspects of Neuroscience as reasonable, from molecules and genetics, through systems, behavior and human diseases of the nervous system.
First year students (BBSP) planning to join Neurobiology typically enroll in NBIO 722 and 723. Also in year one, students take formal instruction in statistics and scientific ethics. Occasionally, BBSP students take courses in cell biology, genetics, or pharmacology and decide to enroll in Neurobiology. In this case students are still required to take NBIO 722 and 723 in year two of the program. Elective requirements are waved for these students. MD/ PhD students joining NBIO will take NBIO 722 and 723 when they begin PhD work after the 2nd year of medical school. Block 6 of 723 and elective requirements are waved for MD/PhD students.
NBIO 722/723 Molecular & Cellular Neuroscience & Systems and Translational Neuroscience
Course Director: Garret Stuber, PhD
The purpose of this year-long course is to present the experimental and theoretical basis for our current understanding of nervous system function and disease. The course fosters an understanding of how we accumulate knowledge and test hypotheses in neuroscience. The course runs as a series of three blocks in the fall and three blocks in the spring. It is team-taught by NBIO faculty who teach sections in their particular areas of expertise, in order to assure students gain optimal understanding of the information. Fall and Spring: Stuber and staff.
During their first year, all BBSP are required to perform 3 research rotations, in different labs of the Curriculum.
NBIO 850 Communication of Scientific Results
(Cross-listed: PHYI 705/706)
Course Director: Spencer Smith, PhD
The class teaches the principles for giving effective talks. The course also covers how to introduce speakers, prepare slides, and speak with the public about science. Spencer Smith currently directs the course, with additional faculty participating in each class. The class is limited to Neurobiology Curriculum students. The fall semester is focused on speaking. Students prepare talks, refine them in small groups (3-4 students), and then present them in class. The in-class talk is videotaped, and these tapes are reviewed by the students in a session with their peers. After another round of refining with their small group, the students give their polished talks to the department in a formal setting. Writing is critiqued in class, with peers and guest faculty all offering input. The videotaped reviews and peer critiquing help tremendously to teach NBIO 850 - Communicating Scientific Results (a.k.a. PClass) effective speaking and writing methods, and this prepares students for the next stage in their scientific careers.
BBSP 710 Statistics for Lab Scientists
Course Director: Eric Bair, PhD
2014-2015 Syllabus & Course Requirements
BBSP 710 introduces the basic concepts and methods of statistics with emphasis on applications in the experimental biological sciences. Students should have a basic understanding of algebra and arithmetic. No previous background in probability or statistics is required, nor is experience with statistical computing. The objectives of this course are to provide graduate students in biomedical research programs familiarity with basic experimental design and elementary statistical methods. By the end of the course, students should understand the principles of experimental design, be familiar with basic statistical methods (and how they are implemented in R), and know which methods are appropriate in a given circumstance.