Required Coursework
First Year
In the first year of study, ALL entering students (GPP and NSGP students alike) enroll in a set of courses at Brown designed to provide fundamental knowledge in neuroscience. In the 2014-15 Academic Year, incoming students will enroll in the following courses:
Advanced Molecular & Cellular Neurobiology I
This course focuses on molecular and cellular approaches used to study the CNS at the level of single molecules, individual cells and single synapses by concentrating on fundamental mechanisms of CNS information transfer, integration, and storage. Topics include biophysics of single channels, neural transmission and synaptic function.
Advanced Molecular & Cellular Neurobiology II
This course continues the investigation of molecular and cellular approaches used to study the CNS from the level of individual genes to the control of behavior. Topics include patterning of the nervous system, generation of neuronal diversity, axonal guidance, synapse formation, the control of behavior by specific neural circuits and neurodegenerative diseases.
Advanced Systems Neuroscience
This course focuses on systems approaches to study nervous system function. Lectures and discussions emphasizes neurophysiology, neuroimaging and lesion analysis in mammals, including humans. Computational approaches will become integrated into the material. Topics include the basic sensory, regulatory, and motor systems. Students will attend NEUR1650, "Structure of the Nervous System," as part of the curriculum.
Advanced Cognitive Neuroscience
This course focuses on cognitive approaches to study nervous system function. Lectures and discussions emphasize neurophysiology, neuroimaging and lesion analysis in mammals, including humans. Computational approaches will become integrated into the material. Topics include the major cognitive systems, including perception, decisions, learning and memory, emotion and reward, language, and higher cortical function.
In addition, all first year students take an intensive laboratory course to gain practical experience in cellular and molecular neuroscience techniques. This 10 day course is held in January at the Marine Biological Laboratories in Woods Hole, MA.
Second Year
In the second year, while at NIH, GPP students must enroll in a graduate level statistics course or demonstrate core competence in statistics. Prior graduate level statistics may suffice. A placement exam will occur at the beginning of each semester. This exam will be administered by the Department of Biostatistics and focus on material in PHP2510. Otherwise, the following course at NIH, offered through the Foundation for Advanced Education in the Sciences (FAES), is recommended :
Statistics for Biomedical Scientists
The objective of this course is to provide an overview of statistics for biomedical research workers and clinicians who are interested in interpretation of the results of statistical analyses. A series of integrated lectures on analysis and interpretation of medical research data. Emphasis is on ideas and understanding rather than mechanics. Topics covered in the first semester include the foundation of statistical logic and the most commonly encountered statistical procedures in medical research. The second semester expands on the material covered in the first semester by looking at assumptions, extensions, and alternatives for common procedures. Those who will be routinely engaged in computing statistical procedures should consider STAT 200. STAT 500 is a full-year course. Material covered in the first semester is necessary to satisfactorily undertake the second semester. Registration is limited to 40 students.
In addition, it may be possible to fulfill the statistics requirement through distance learning by taking one of the following courses at Brown, with the professor's approval:
Principles of Biostatistics and Data Analysis
Intensive first course in biostatistical methodology, focusing on problems arising in public health, life sciences, and biomedical disciplines. Summarizing and representing data; basic probability; fundamentals of inference; hypothesis testing; likelihood methods. Inference for means and proportions; linear regression and analysis of variance; basics of experimental design; nonparametrics; logistic regression.
Recent Applications of Probability and Statistics
This topics course covers modern applications of probability and statistics in the computational, cognitive, engineering, and neural sciences. Topics include: Markov chains and their applications to MCMC computing and hidden Markov models; Dependency graphs and Bayesian networks; parameter estimation and the EM algorithm; Nonparametric statistics ("learning theory"), including consistency, bias/variance tradeoff, and regularization; Gibbs distributions, maximum entropy, and their connections to large deviations. Each topic will be introduced with several lectures on the mathematical underpinnings, and concluded with a computer project, carried out by each student individually, demonstrating the mathematics and utility of the approach.
Brown University Graduate School requires students to complete 24 course credits before graduation. Typically these credits are earned within the first three years of study. To satisfy any course requirement, students must earn a grade of B or higher. Students receiving grades of C or No-Credit (C and NC) must meet with their First Year Advisory or Thesis Committee to discuss remedial action.
Optional Courses
Students may also select from a large number of courses and seminars offered at NIH through the Foundation for Advanced Education in the Sciences (FAES) and by the major departments in the program at Brown including Neuroscience, Cognitive, Linguistic and Psychological Sciences, Applied Mathematics, Engineering, Molecular Biology, Cell Biology and Biochemistry, and Molecular Pharmacology, Physiology and Biotechnology. These courses are chosen to enhance students’ laboratory research experience. During the second year and beyond, it may be possible for GPP students to participate in Brown courses electronically at a distance, with approval of the professor and the department.